Multitask Learning for Large-scale Semantic Change Detection

Rodrigo Caye Daudta,b, Bertrand Le Sauxa, Alexandre Boulcha, Yann Gousseaub

aDTIS, ONERA, Universite Paris-Saclay, FR-91123 Palaiseau, FrancebLTCI, Telecom ParisTech, FR-75013 Paris, France

Abstract

Change detection is one of the main problems in remote sensing, and is essential to the accurate processing andunderstanding of the large scale Earth observation data available. Most of the recently proposed change detectionmethods bring deep learning to this context, but change detection labelled datasets which are openly available are stillvery scarce, which limits the methods that can be proposed and tested. In this paper we present the first large scalevery high resolution semantic change detection dataset, which enables the usage of deep supervised learning methodsfor semantic change detection with very high resolution images. The dataset contains coregistered RGB image pairs,pixel-wise change information and land cover information. We then propose several supervised learning methodsusing fully convolutional neural networks to perform semantic change detection. Most notably, we present a networkarchitecture that performs change detection and land cover mapping simultaneously, while using the predicted landcover information to help to predict changes. We also describe a sequential training scheme that allows this network tobe trained without setting a hyperparameter that balances different loss functions and achieves the best overall results.

1. Introduction

One of the main purposes of remote sensing is the ob-servation of the evolution of the land. Satellite and aerialimaging enables us to keep track of the changes that oc-cur around the globe, both in densely populated areas aswell as in remote areas that are hard to reach. That is whychange detection is a problem so closely studied in thecontext of remote sensing (Coppin et al., 2004). Changedetection is the name given to the task of identifying areasof the Earth’s surface that have experienced changes byjointly analysing two or more coregistered images (Bruz-zone and Bovolo, 2013). Changes can be of several differ-ent types depending on the desired application, e.g. thosecaused by natural disasters, urban expansion, and defor-estation. In this paper we treat change detection as a denseclassification problem, aiming to predict a label for each

This work was originally submitted under the title ”High ResolutionSemantic Change Detection”, and had its title changed during the reviewprocess.

pixel in an input image pair, i.e. achieving semantic seg-mentation.

The search for ever more accurate change detectioncomes from the value of surveying large amounts of landand analysing its evolution over a period of time. De-tecting changes manually is a slow and laborious pro-cess (Singh, 1989) and the problem of automatic changedetection using image pairs or sequences has been stud-ied for many decades. The history of change detectionalgorithms and overviews of the most important methodsare described in the reviews Singh (1989) and Hussainet al. (2013). Throughout the years, change detection ben-efited a lot from computer vision and image processingadvances. In recent years, computer vision made tremen-dous progress thanks to machine learning techniques, andthese were used for solving a wide range of problems re-lated to image understanding (LeCun et al., 2015).

The rise of these techniques is explained by three mainfactors. First, the hardware required for the large amountsof calculations that are often required for machine learn-ing techniques is becoming cheaper and more powerful.

Preprint submitted to Computer Vision and Image Understanding August 29, 2019

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Second, new methods are being proposed to exploit thedata in innovative ways. Finally, the amount of availabledata is increasing, which is essential for many machinelearning techniques.

In this paper we propose a versatile supervised learningmethod to perform pixel-level change detection from im-age pairs based on state-of-the-art computer vision ideas.The proposed method is able to perform both binaryand semantic change detection using very high resolution(VHR) images. Binary change detection attempts to iden-tify which pixels correspond to areas where changes haveoccurred, whereas semantic change detection attempts tofurther identify the type of change that has occurred ateach location. The proposed method is able to performchange detection using VHR images from sources suchas WorldView-3, Pleiades and IGN’s BD ORTHO. As wasdescribed by Hussain et al. (2013) and Bruzzone and Bo-volo (2013), VHR change detection involves several extrachallenges.

A new VHR semantic change detection dataset of un-precedented size is also presented in this paper. Thisdataset will be released publicly to serve as a benchmarkand as a research tool for researchers working on changedetection. The methods used to create this dataset, as wellas the limitations of the available data, will be describedlater on. Until now, the most advanced ideas brought tocomputer vision by deep learning techniques could not beapplied to change detection due to the lack of large anno-tated datasets. This dataset will enable the application ofmore sophisticated machine learning techniques that wereheretofore too complex for the amount of change detec-tion data available.

2. Related work

The work presented in this paper is based on severaldifferent ideas coming from two main research areas:change detection and machine learning. This section con-tains a discussion about the works hat have more heav-ily influenced this work, providing details about unsu-pervised methods, supervised learning, and fully convo-lutional networks for semantic segmentation.

Change detection algorithms usually comprise twomain steps (Singh, 1989; Hussain et al., 2013). First, adifference metric is proposed so that a quantitative mea-surement of the difference between corresponding pixels

can be calculated. The image generated from this stepis usually called a difference image. Second, a thresh-olding method or decision function is proposed to sepa-rate the pixels into ”change” and ”no change” based onthe difference image. These two steps are usually in-dependent. Post-processing and pre-processing methodsare sometimes used to improve results. Many algorithmsuse out-of-the-box registration algorithms and focus onthe other main steps for change detection (Hussain et al.,2013). Most papers on change detection propose either anovel image differencing method (Bovolo and Bruzzone,2005; El Amin et al., 2016, 2017; Zhan et al., 2017) or anovel decision function (Bruzzone and Prieto, 2000; Ce-lik, 2009). A well established family of change detectionmethods is change vector analysis (CVA), considering themultispectral difference vector in polar or hypersphericalcoordinates and attempting to characterise the changesbased on the associated vectors at each pixel (Lambinand Strahlers, 1994; Bovolo and Bruzzone, 2007; Hussainet al., 2013). Most methods that propose image differ-encing techniques followed by thresholding assume thata threshold is chosen based on the difference image. Theauthors of Hussain et al. (2013) and Rosin and Ioannidis(2003) noted that the performance of such algorithms isscene dependent.

Hussain et al. (2013) categorise change detection al-gorithms into two main groups: pixel based and objectbased change detection. The former are attempts to iden-tify whether or not a change has occurred at each pixelin the image pair, while the latter methods attempt to firstgroup pixels that belong to the same object and use infor-mation such as the object’s colour, shape and neighbour-hood to help determine if that object has been changed be-tween the acquisitions. Change detection algorithms canalso be split into supervised and unsupervised groups.

As noted by Hussain et al. (2013) and Bruzzone andBovolo (2013), change detection on low resolution im-ages and on VHR images face different challenges. In lowresolution images, pixels frequently contain informationabout several objects contained within its area. In suchcases, a pixel in an image pair may contain both changedand unchanged surfaces simultaneously. VHR images aremore susceptible to problems such as parallax, high re-flectance variability for objects of the same class, and co-registration problems (Bruzzone and Bovolo, 2013). Itfollows that algorithms that perform change detection on

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(a) Image 1. (b) LCM 1. (c) Image 2. (d) LCM 2. (e) Change map.

(f) Image 1. (g) LCM 1. (h) Image 2. (i) LCM 2. (j) Change map.

Figure 1: Examples of image pairs, land cover maps (LCM) and associated pixel-wise change maps from the HRSCD dataset. In the depictedLCMs, blue represents the ”artificial surfaces” class, and orange represents the ”agricultural areas” class.

very high resolution images must be aware of not onlya given pixel’s values, but also of information about itsneighbourhood.

Machine learning algorithms, and notably convolu-tional neural networks (CNNs) in recent years, also havehad great impact. For examples, in remote sensing, CNNswere used for road detection (Mnih and Hinton, 2010),and in computer vision, CNNs were used on the re-lated task of comparing image pairs (Chopra et al., 2005;Zagoruyko and Komodakis, 2015). We now examine indetails unsupervised and supervised machine learning ap-proaches, the latter category being then subdivided instandard techniques, CNNs and Fully-Convolutional Neu-ral Networks.

Unsupervised methods have been used for changedetection in many different ways (Hussain et al., 2013;Vakalopoulou et al., 2015; Liu et al., 2019). In thecontext of change detection, annotated datasets are ex-tremely scarce and often kept private. Thus, unsuper-vised methods are extremely useful, since, unlike super-vised methods, they do not need labelled data for train-ing. Many of these methods automatically analyse thedata in difference images and detect patterns that corre-spond to changes (Bazi et al., 2005; Bruzzone and Pri-eto, 2000). Other methods use unsupervised learning ap-proaches such as iterative training (Liu et al., 2016), au-toencoders (Zhao et al., 2014), and principal componentanalysis with k-means clustering (Celik, 2009) to separate

changed pixels from unchanged ones.Supervised change detection algorithms require la-

belled training data from which the task of change detec-tion can be learned. Several methods have been proposedfor performing change detection using supervised learn-ing algorithms such as support vector machines (Huanget al., 2008; Volpi et al., 2009, 2013; Le Saux and Randri-anarivo, 2013), random forests (Sesnie et al., 2008), andneural networks (Gopal and Woodcock, 1996; Dai andKhorram, 1999; Zhao et al., 2014). CNN architectureshave also been proposed to perform supervised changedetection (Zhan et al., 2017; Chen et al., 2018b).

Convolutional neural networks (CNNs) for change de-tection have been proposed by different authors in the re-cent years. The majority of these methods avoid the prob-lem of the lack of data by using transfer learning tech-niques, i.e. using networks that have been pre-trainedfor a different purpose on a large dataset (El Amin et al.,2016, 2017). While transfer learning is a valid solution,it is also limiting. Firstly, end-to-end training tends toachieve the best results for a given problem when possi-ble. Transfer learning also assumes that all images areof the same type. As most large scale datasets containRGB images, this means that extra bands contained inmultispectral images must be ignored. It has howeverbeen shown that using all available multispectral bandsfor change detection leads to better results (Daudt et al.,2018b).

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Several works have used CNNs to generate the differ-ence image that was described earlier, followed by tradi-tional thresholding methods on those images. El Aminet al. (2016, 2017) proposed using the activation of pre-trained CNNs to generate descriptors for each pixel, andusing the Euclidean distance between these descriptors tobuild the difference image. Zhan et al. (2017) trained anetwork to produce a 16-dimensional descriptor for eachpixel. Descriptors were similar for pixels with no changeand dissimilar for pixels that experienced change. Liuet al. (2016) used deep belief networks to generate pixeldescriptors from heterogeneous image pairs, then the Eu-clidean distance is used to build a difference image. Zhaoet al. (2014) proposed a deep belief network that takes intoaccount the context of a pixel to build its descriptor. Mouet al. (2019) proposed using patch based recurrent CNNsto detect changes in image pairs. CNNs for change detec-tion have also been studied outside the context of remotesensing, such as surface inspection (Stent et al., 2015).

Fully convolutional neural networks (FCNNs) are atype of CNNs that are especially suited for dense pre-diction of labels and semantic segmentation (Long et al.,2015). Unlike traditional CNNs, which output a singleprediction for each input image, FCNNs are able to pre-dict labels for each pixel independently and efficiently.Ronneberger et al. (2015) proposed a simple and elegantaddition to FCNNs that aims to improve the accuracy ofthe final prediction results. The proposed idea is to con-nect directly layers in earlier stages of the network to lay-ers at later stages to recover accurate spatial informationof region boundaries. FCNNs currently achieve state-of-the-art results in semantic segmentation problems, includ-ing those in remote sensing (Volpi and Tuia, 2017; Mag-giori et al., 2017; Chen et al., 2018a).

Fully convolutional networks trained from scratch toperform change detection were proposed for the first timeby Daudt et al. (2018a). Both Siamese and early fusionarchitectures were compared, expanding on the ideas pro-posed earlier by Chopra et al. (2005) and Zagoruyko andKomodakis (2015). A similar approach was simultane-ously proposed by Chen et al. (2018b) outside the contextof remote sensing. To the best of our knowledge, the onlyother time a fully convolutional Siamese network has beenproposed was by Bertinetto et al. (2016) with the purposeof tracking objects in image sequences.

3. Dataset

Research on the problem of change detection is hin-dered by a lack of open datasets. Such datasets are essen-tial for a methodical evaluation of different algorithms.Benedek and Sziranyi (2009) created a binary changedataset with 13 aerial image pairs split into three regionscalled the Air Change dataset. A dataset, called ONERASatellite Change Detection (OSCD) dataset, composed of24 multispectral image pairs taken by the Sentinel-2 satel-lites is presented in (Daudt et al., 2018b). Both of thesedatasets allow for simple machine learning techniques tobe applied to the problem of change detection, but withthese small amounts of images overfitting becomes oneof the main concerns even with relatively simple models.The Aerial Imagery Change Detection (AICD) datasetcontains synthetic aerial images with artificial changesgenerated with a rendering engine (Bourdis et al., 2011).These datasets do not contain semantic information aboutthe land cover of the images, and contain either low reso-lution (OSCD, Air Change) or simulated (AICD) images.

For this reason, we have created the first large scaledataset for semantic change detection, which we presentin this section. The High Resolution Semantic ChangeDetection (HRSCD) dataset will be released to the sci-entific community to be used as a benchmark for seman-tic change detection algorithms and to open the doors tothe usage of state-of-the-art deep learning algorithms inthis context. The dataset contains not only informationabout where changes have taken place, but also seman-tic information about the imaged terrain in all images ofthe dataset. Examples of image pairs, land cover maps(LCM) and change maps taken from the dataset are de-picted in Fig. 1.

3.1. Images

The dataset contains a total of 291 RGB image pairsof 10000x10000 pixels. These are mosaics of aerial im-ages taken by the French National Institute of Geograph-ical and Forest Information (IGN). The image pairs con-tain an earlier image acquired in 2005 or 2006, and a sec-ond image acquired in 2012. They come from a databasenamed BD ORTHO which contains orthorectified aerialimages of several regions of France from different yearsat a resolution of 50 cm per pixel. The 291 selected im-age pairs are all the images in this database that satisfy the

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conditions for the labels, which will be described below.The images cover a range of urban and countryside areasaround the French cities of Rennes and Caen.

The dataset contains more than 3000 times more anno-tated pixel pairs than either OSCD or Air Change datasets.Also, unlike these datasets, the labels contain informa-tion about the types of change that have occurred. Fi-nally, labels about the land cover of the images in thedataset are also available. This is much more data thanwas previously available in the context of change detec-tion and it opens the doors for many new ideas to betested. The amount of labelled pixels and surface areafor land cover classification is also about 8 times larger inthe proposed HRSCD dataset than in the DeepGlobe LandCover Classification dataset (Demir et al., 2018), both ofthe datasets containing images of the same spatial resolu-tion (50 cm/px).

The BD ORTHO images provided by IGN are availablefor free for research purposes, but not all images can beredistributed by the users. That is the case for the im-ages taken in 2005 and 2006. Nevertheless, we will makeavailable all the data for which we have the rights of re-distribution and the rasters that we have generated for se-mantic change detection and land cover mapping. Thedataset will also contain instructions for downloading theremaining images that are necessary for using the datasetdirectly from IGN’s website.

3.2. LabelsThe labels in the dataset come from the European En-

vironment Agency’s (EEA) Copernicus Land MonitoringService - Urban Atlas project. It provides ”reliable, inter-comparable, high-resolution land use maps” for func-tional urban areas in Europe with more than 50000 in-habitants. These maps were generated for the years of2006 and 2012, and a third map is available containingthe changes that took place in that period. Only the im-ages in the regions mapped in the Urban Atlas project andwith a maximum temporal distance of one year were keptin the dataset.

The available land cover maps are divided in severalsemantic classes, which are in turn organised in differenthierarchical levels. By grouping the labels at different hi-erarchical levels it is possible to generate maps that aremore coarsely or finely divided. For example, groupingthe labels with the coarsest hierarchical level yields five

Table 1: Urban Atlas land cover mapping classes at hierarchical level L1Code Class0 No information1 Artificial surfaces2 Agricultural areas3 Forests4 Wetlands5 Water

classes (plus the ”no information” class) shown in Table1. This hierarchical level will henceforth be referred to asL1.

These maps are openly available in vector form online.We have used these vector maps and the georeferencedBD ORTHO images to generate rasters of the vector mapsthat are aligned with the rasters of the images. Theserasters allow us to have ground truth information abouteach pixel in the dataset.

It is important to note that there are slight differences inthe semantic classes present in Urban Atlas 2006 and inUrban Atlas 2012. These differences do not affect the L1hierarchical grouping and therefore had no consequencein the work presented later in this paper. It may neverthe-less affect future works done with the data. We leave it upto the users how to best interpret and deal with these dif-ferences. More information will be provided in the datasetfiles.

3.3. Dataset analysis

Despite its unprecedented size and qualities, we ac-knowledge in this section the dataset’s limitations andchallenges. Nevertheless, we will show later in this pa-per that despite these limitations, the dataset allows forthe boundaries of the state-of-the-art in semantic changedetection through machine learning to be pushed.

One issue is the accuracy of the labels contained in theUrban Atlas vector maps with respect to the BD ORTHOimages. We do not have access to the images used to buildthe Urban Atlas vector maps, nor to the exact dates oftheir acquisitions, nor to the dates of acquisition of theimages in BD ORTHO. Hence, there are some discrepan-cies between the information in the vector maps and inthe images. Furthermore, EEA only guarantees a mini-mum label accuracy of 80-85% depending on the consid-

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(a) Image 1. (b) Image 2. (c) Inaccurate border.

(d) Image 1. (e) Image 2. (f) False negative.

(g) Image 1. (h) Image 2. (i) False positive.

Figure 2: Examples of: ((a)-(c)) overly large change markings, ((d)-(f))failure to mark changes, ((g)-(i)) false positive.

ered class. Most of the available data is accurate, but it isimportant to consider that the labels in the dataset are notflawless. Examples of false negatives and false positivescan be see in Fig. 2 (d)-(f) and Fig. 2 (g)-(i), respectively.It is also worth noting that the labels have been createdusing previously known vector maps, mostly by labellingcorrectly each of the known regions. This means a sin-gle label was given to each region, and this led to inac-curate borders in some cases. This can be clearly seen inFig. 2 (a)-(c).

One of the main challenges involved in using thisdataset for supervised learning is the extreme label im-balance. As can be seen in Table 2, 99.232% of all pixelsare labelled as no change, and the largest class is fromagricultural areas to artificial surfaces (i.e. class 2 to class1), which accounts for 0.653% of all pixels. These twoclasses together account for 99.885% of all pixels, whichmeans all other change types combined account for only0.115% of all pixels. Furthermore, many of the possibletypes of change have no examples at all in any of the im-ages of the dataset. It is of paramount importance when

Table 2: Change class imbalance at hierarchical level L1. Row num-ber represents class in 2006, column number represents class in 2012.Classes were defined in Table 1.

1 2 3 4 51 0% 0.011% 0% 0.001% 0.001%2 0.653% 0% 0.001% 0% 0.077%3 0.014% 0.002% 0% 0% 0%4 0% 0% 0% 0% 0%5 0.001% 0.004% 0% 0.004% 0%

No change 99.232%

using this dataset to take into account this imbalance. Thisalso means that using the overall accuracy as a perfor-mance metric with this dataset is not a good choice, as itvirtually only reflects how many pixels of the no changeclass have been classified correctly. Other metrics, suchas Cohen’s kappa coefficient or the Sørensen-Dice coeffi-cient, must be used instead. This class imbalance is char-acteristic of real world large scale data, where changes aremuch less frequent than unchanged surfaces. Therefore,this dataset provides a realistic evaluation tool for changedetection methods, unlike carefully selected image pairswith large changed regions.

The problem of supervised learning using noisy labelshas already been studied and evidence suggests that su-pervised learning with noisy labels is possible as long asa dataset of a large enough size is used (Rolnick et al.,2017). Other works attempt to explicitly deal with thenoisy labels present in the dataset and prioritise the cor-rect labels during training (Maggiolo et al., 2018).

Finally, we acknowledge how challenging it is to usehierarchical levels finer than L1 due to: 1) a massive in-crease in the number of possible changes, and 2) the dif-ference between similar classes becomes more abstractand context based. For example, the difference betweenthe ”Discontinuous Medium Density Urban Fabric” andthe ”Discontinuous Low Density Urban Fabric” classesdefined in Urban Atlas depends not only in correctly iden-tifying the surface at a given pixel (e.g. building or grass),but also by understanding the surroundings of the pixeland calculating the ratio between these two classes at agiven neighbourhood that is not clearly defined.

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4. Methodology

4.1. Binary change detectionWe have already showed in a previous work the effi-

cacy of using three different architectures of fully con-volutional neural networks for change detection (Daudtet al., 2018a). Chen et al. (2018b) simultaneously pro-posed a fully convolutional architecture for change de-tection that is very similar to one of the three initiallyproposed architectures. In both of these works, FCNNarchitectures performed better than previous methods forchange detection.

Building on this previous work, we have modified theFC-EF architecture proposed in Daudt et al. (2018a) touse residual blocks, as proposed by He et al. (2016). Theresulting network is later referred to as FC-EF-Res, andis depicted in Fig. 3. These residual blocks were used inan encoder-decoder architecture with skip connections toimprove the spatial accuracy of the results (Ronnebergeret al., 2015). These residual blocks were chosen to fa-cilitate the training of the network, which is especiallyimportant for its deeper variations that will be discussedlater.

Conv 1

x1

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Figure 3: FC-EF-Res architecture, used for tests with smaller datasets toavoid overfitting. Using residual blocks improves network performanceand facilitates training.

When testing on the OSCD dataset (Section 5.1), thesize of the network has been kept approximately the sameas in Daudt et al. (2018a) to avoid overfitting. When us-ing the proposed HRSCD dataset (Section 5.2), the largeramount of annotated pixels allows us to use deeper and

more complex models. In that case, the number of encod-ing levels and residual blocks per level has been increased,but the idea behind the network is the same as of FC-EF-Res.

4.2. Semantic change detection

As was mentioned earlier, the efficiency of the pro-posed architecture for binary change detection and theavailability of the HRSCD dataset enable us to tackle theproblem of semantic change detection. This problem con-sists of two separate but not independent parts. The firsttask is analogue to binary change detection, i.e. we at-tempt to determine whether a change has occurred at eachpixel in a co-registered multi-temporal image pair. Thesecond task is to differentiate between types of changes.In our case, this consists of predicting the class of the pixelin each of the two given images. The problem of semanticchange detection lies in the intersection between changedetection and land cover mapping.

Below we will describe four different intuitive strate-gies to perform semantic change detection using deepneural networks. Starting from the plain comparison ofland cover maps, we then develop more involved strate-gies. These strategies vary in complexity and perfor-mance, as will be discussed in Section 5.

4.2.1. Strategy 1: Direct comparison of LCMsThe problem of automatic land cover mapping is a well

studied problem. In particular, methods involving CNNshave recently been proposed, yielding good performances(Audebert et al., 2016). When the land cover informationis available, as it is the case in the HRSCD dataset, themost intuitive method that can be proposed for semanticchange detection would be to train a land cover mappingnetwork and to compare the results for pixels in the imagepair (see Fig. 4(a)).

The advantage of this method is its simplicity. In manycases we could assume changes occurred where the pre-dicted class label differs between the two images, and thetype of change is given by the predicted labels at eachof the two acquisition moments. The weakness of thismethod is that it heavily depends on the accuracy of thepredicted land cover maps. While modern FCNNs areable to map areas to a good degree of accuracy, there arestill many wrongly predicted labels, especially around the

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(a) Strategy 1: semantic CD from land cover maps. (b) Strategy 2: direct semantic CD.

(c) Strategy 3: separate CD and LCM. (d) Strategy 4: integrated CD and LCM.

Figure 4: Schematics for all four proposed strategies for semantic change detection. Φ represents the network branch’s learnable parameters, ”Enc”means encoder, ”Dec” means decoder, ”LCM” means land cover mapping, and ”CD” means change detection.

boundaries between regions of different classes. Further-more, when comparing the results for two acquisitions theprediction errors would accumulate. This means the ac-curacy of this change detection algorithm would be lowerthan the land cover mapping network, and would likelypredict changes in the borders between classes simply dueto the inaccuracy of the network.

4.2.2. Strategy 2: Direct semantic CDA second intuitive approach is to treat each possible

type of change as a different and independent label, con-sidering semantic change detection as a simple semanticsegmentation along the lines of what has been done to bi-nary change detection in the past (Daudt et al., 2018a).

The weakness of this method is that the number ofchange classes grows proportionately to the square of thenumber of land cover classes that is considered. This,combined with the class imbalance problem that was dis-cussed earlier, proves to be a major challenge when train-ing the network.

4.2.3. Strategy 3: Separate LCM and CDSince it has been proven before that FCNNs are able

to perform both binary change detection and land covermapping, a third possible approach is to train two sepa-rate networks that together perform semantic change de-tection (see Fig. 4(c)). The first network performs binarychange detection on the image pair, while the second net-work performs land cover mapping of each of the inputimages. The two networks are trained separately sincethey are independent.

In this strategy, the two input images produce three out-puts: two land cover maps and a change map. At eachpixel, the presence of change is predicted by the changemap, and the type of change is defined by the classes pre-dicted by the land cover maps at that location. This waythe number of predicted classes is reduced relative to theprevious strategy (i.e. the number of classes is no longerproportional to the square of land cover classes) withoutloss of flexibility. This helps with the class imbalanceproblem. It also avoids the problem of predicting changesat every pixel where the land cover maps differ, since the

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Table 3: Summary of proposed change detection strategies.Str. Description Training1 Diff. of LCMs LCM supervision2 Direct semantic CD Multiclass CD supervision3 Separate CD and LCM Separate LCM and CD

4.1 Integrated CD and LCM Triple loss function4.2 Integrated CD and LCM Sequential training

change detection problem is treated separately from landcover mapping.

We argue that such network may be able to identifychanges of types it has not seen during training, as longas it has seen the land cover classes during training. Forexample, the network could in theory correctly classifya change from agricultural area to wetland even if suchchanges are not in the training set, as long as it has enoughexamples of those classes to correctly classify them in theland cover mapping branches. The combination of twoseparate networks allows us to split the problem into two,and optimise each part to maximise performance.

4.2.4. Strategy 4: Integrated LCM and CDThe last of the proposed approaches is an evolution of

the previous strategy of using two FCNNs for the tasksof binary change detection and land cover mapping. Wepropose to integrate the two FCNNs into a single multi-task network (see Fig. 4(d) and Fig. 5) so that land coverinformation can be used for change detection. The com-bined network takes as input the two co-registered imagesand outputs three maps: the binary change map and thetwo land cover maps.

In the proposed architecture, information from the landcover mapping branches of the network is passed to thechange detection branch of the network in the form ofdifference skip connections, which was shown to be themost effective form of skip connections for Siamese FC-NNs (Daudt et al., 2018a). The weights of the two landcover mapping branches are shared since they perform anidentical task, allowing us to significantly reduce the num-ber of learned parameters.

This multipurpose network gives rise to a new issueduring the training phase. Given that the network outputsthree different image predictions, it is necessary to bal-ance the loss functions from these results. Since two of

Table 4: Definitions of metrics used for evaluating results quantitatively.Legend: TP - true positive, TN - true negative, FP - false positive, FN -false negative, po - observed agreement between ground truth and pre-dictions, pe - expected agreement between ground truth and predictionsgiven class distributions.

Tot. acc. (T P + T N)/(T P + T N + FP + FN)Precision T P/(T P + FP)

Recall T P/(T P + FN)Dice 2 · T P/(2 · T P + FP + FN)

Kappa (po − pe)/(1 − pe)

the outputs have exactly the same nature (the land covermaps), it follows from the symmetry of these branchesthat they can be combined into a single loss function bysimple addition. The question remains on how to balancethe binary change detection loss function and the landcover mapping loss function to maximise performance.

We have proposed and tested two different strategies fortraining the network. The first and more naive approachto this problem is to minimise a loss function that is aweighted combination of the two loss functions. This lossfunction would have the form

Lλ(ΦEnc,CD,ΦDec,CD,ΦEnc,LCM,ΦDec,LCM)=L(ΦEnc,CD,ΦDec,CD) + λL(ΦEnc,LCM,ΦDec,LCM)

(1)

where Φ represents the various network branch parame-ters, and L is a pixel-wise loss function. In this work, thepixel-wise cross entropy function was used as loss func-tion as is traditional in semantic segmentation problems.The problem then becomes the search for the value of λthat leads to the best balance between the two loss terms.This can be found through a grid search, but the test ofeach value of λ is done by training the whole networkuntil convergence, which is a slow and costly procedure.This will later be referred to as Strategy 4.1.

To reduce the aforementioned training burden, we pro-pose a second approach to train the network that avoidsthe need of setting the hyperparameter λ. We train thenetwork in two stages. First, we consider only the landcover mapping loss

L1(ΦEnc,CD,ΦDec,CD,ΦEnc,LCM,ΦDec,LCM)=L(ΦEnc,LCM,ΦDec,LCM)

(2)

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Conv 1

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ReLU

ΦEnc,LCM

ΦEnc,LCM ΦDec,LCM

ΦEnc,CD ΦDec,CD

ΦDec,LCM

Figure 5: Detailed schematics for the integrated change detection and land cover mapping network (Strategy 4). The encoder-decoder architectureis the same that was used for all 4 strategies.

and train only the land cover mapping branches of the net-work, i.e. we do not train ΦEnc,CD or ΦDec,CD at this stage.Since the change detection branch has no influence on theland cover mapping branches, we can train these branchesto achieve the maximum possible land cover mapping per-formance with the given architecture and data. Next, weuse a second loss function based only on the change de-tection branch:

L2(ΦEnc,CD,ΦDec,CD,ΦEnc,LCM,ΦDec,LCM)=L(ΦEnc,CD,ΦDec,CD)

(3)

while keeping the weights for the land cover mappingΦEnc,LCM and ΦEnc,LCM fixed. This way, the change de-tection branch learns to use the predicted land cover in-formation to help to detect changes without affecting landcover mapping performance. This will later be referred toas Strategy 4.2.

5. Results

5.1. Multispectral change detectionWe first evaluate the performance of the proposed FC-

EF-Res network. As explained in Section 4.1, this net-work is an evolution of the convolutional architecture FC-EF proposed in Daudt et al. (2018a), to which residualblocks have been added in place of traditional convolu-tional layers.

The FC-EF-Res architecture was compared to thepreviously proposed FCNN architectures on the OSCDdataset for binary change detection, which contains lower-resolution Sentinel-2 image pairs with 13 multispectral

bands. As expected, the residual extension of the FC-EFarchitecture outperformed all previously proposed archi-tectures. The difference was noted on both the RGB andthe multispectral cases. On the RGB case, the improve-ment was of such magnitude that the change detectionperformance on RGB images almost matched the perfor-mance on multispectral images. The results can be seenin Table 5. This corroborates the claims made by He et al.(2016) that using residual blocks improves the trainingperformance of CNNs. For this reason, all networks thatare tested with the HRSCD dataset use residual modules.

5.2. Very high resolution semantic change detection

To test the methods proposed in Section 4.2 we splitthe HRSCD images into two groups: 146 image pairs fortraining and 145 image pairs for testing. By splitting thetrain and test sets this way we can ensure that no pixel inthe test set has been seen during training. Class weightswere set inversely proportional to the number of trainingexamples to counterbalance the dataset’s class imbalance.The results for each of the proposed strategies can be seenin Table 6, and illustrative image results can be seen inFig. 6.

As is the case for most deep neural networks, the train-ing times for the proposed methods are significantly largerthan the testing times. Once the network has been trained,its fast inference speed allows it to process large amountsof data efficiently. The proposed methods took 3-5 hoursof training time using a GeForce GTX 1080 Ti GPU with11GB of memory. Inference times of the proposed meth-ods were under 0.04 s for 512x512 image pairs using the

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Table 5: Change detection results of several methods on the OSCD dataset, for the RGB and multispectral (MS) cases. Results are in percent.Data Network Prec. Recall Tot. acc. Dice

RG

B

FC-EF 44.72 53.92 94.23 48.89FC-Siam-conc 42.89 47.77 94.07 45.20FC-Siam-diff 49.81 47.94 94.86 48.86FC-EF-Res 52.27 68.24 95.34 59.20

MS

FC-EF 64.42 50.97 96.05 56.91FC-Siam-conc 42.39 65.15 93.68 51.36FC-Siam-diff 57.84 57.99 95.68 57.92FC-EF-Res 54.93 66.48 95.64 60.15

same hardware.In Strategy 1, which naively attempts to predict change

maps from land cover maps, we can see that the networksucceeds in accurately classifying the imaged terrains, butthis is not enough to predict accurate change maps. Thechange detection kappa coefficient for this strategy is verylow, which means this method is marginally better thanchance for change detection.

The results for Strategy 2 are a fair improvement overthose of Strategy 1. The change detection Dice coefficientand the land cover mapping results for this method are notreported due to its nature, since Dice coefficients can onlybe calculated for binary classification problems, and thisstrategy bypasses the land cover mapping steps. Despiteachieving a higher kappa coefficient, the network learnedto always predict the same type of change where changesoccurred. This means that despite using appropriatelytuned class weights, the learning process did not succeedin overcoming the extreme class imbalance present in thedataset. In other words, the network learned to detectchanges but no semantic information was present in theresults.

For Strategy 3, the land cover mapping network thatwas used was the same as that of Strategy 1, whichachieved good performance. A binary change detectionnetwork was trained to be used for masking the land covermaps. The performance of this network was better thanthat of Strategy 1 but worse than that of Strategy 2. Theresults show that this is due to an overestimation of thechange class. This shows once again how challengingdealing with the extreme class imbalance is.

The results of Strategy 4 are the best ones overall. Thesimultaneous training strategy (Str. 4.1) achieves excel-

lent performance in both land cover mapping and changedetection, proving the viability of this strategy. The re-ported results were obtained with λ = 0.05, which isa value that prioritises the training of the change detec-tion branch of the network. We then see that the samenetwork trained with sequential training (Str. 4.2) ob-tained even better results in both change detection andland cover mapping without needing to search for an ad-equate parameter λ. This, according to our results, is thebest semantic change detection method. By comparingthe results for Strategies 3 and 4 we can see the improve-ments that result directly from integrating the change de-tection and land cover mapping branches of the networks.In other words, Strategy 4.2 allows us to maximise thechange detection performance without reducing the landcover mapping accuracy.

The best performing land cover mapping method wasthe single purpose network that was trained and used forStrategies 1 and 3. The fact that it achieves a better kappacoefficient than Strategy 4.2 is merely due to the random-ness of the initialisation and training of the network, as theland cover mapping branches of Strategy 4.2 are identicalto those used in Strategies 1 and 3. This also explains whytheir results are so similar. By comparing these results tothose of Strategy 4.1 it emphasises once again the fact thatattempting to train the network shown in Fig. 5 all at oncedamages performance in both change detection and landcover mapping.

In Fig. 6 we can see the results of the proposed net-works on a pair of images from the dataset. Note theamount of false detections by Strategy 1 due to the lack ofaccuracy of prediction of the land cover maps on regionboundaries. The second row shows the predicted classes

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(a) Image 1 (b) Image 2 (c) CD - GT (d) Str. 1 (e) Str. 2 (f) Str. 3 (g) Str. 4.1 (h) Str. 4.2

(i) LCM 1 - GT (j) Str. 1/3 (k) Str. 4.1 (l) Str. 4.2 (m) LCM 2 - GT (n) Str. 1/3 (o) Str. 4.1 (p) Str. 4.2

Figure 6: Illustrative images of the obtained results: (a)-(b) multitemporal image pair; (c) ground truth change detection map; (d)-(h) predictedchange maps; (i)-(l) ground truth and predicted land cover maps for image 1; (m)-(p) ground truth and predicted land cover maps for image 2.

at each pixel for each image. The semantic informationabout the changes comes from comparing these two pre-dictions. For example, comparing the images in Fig. 6(k) and (o) we can say that the changes predicted in (g)were from the ”Agricultural areas” class to the ”Artificialsurfaces” class.

In our tests we observed that the trained networkshad the tendency to overestimate the size of the detectedchanges. It is likely that this happens simply due to thenature of the data that was used for training. The labels inthe HRSCD dataset, which come from Urban Atlas, markas a change the whole terrain where a change of class hap-pened. This means that not only the pixels associated witha given change are marked as change, but the neighbour-ing pixels that are in the same parcel are also marked aschange. This leads to the networks learning to overesti-mate the boundary of the detected changes in an attemptto also correctly classify the pixels surrounding the de-tected change. This once again reflects the challenges ofthe HRSCD dataset.

The performance of two state-of-the-art CD methodsare also shown in Table 6. The first method, proposedby El Amin et al. (2016), is based on transfer learningand uses features from a pretrained VGG-19 model (Si-monyan and Zisserman, 2015) to create pixel descriptors,whose Euclidean distance is used to build a difference im-age. The original method uses Otsu thresholding to per-form CD, but we have found that such approach leadsto overestimating changes. We therefore tuned a fixedthreshold (T = 2300) using a few example images and

used that value to test the algorithm on all test data, whichsignificantly increased its performance by reducing falsepositives. Also included are the results by the method pro-posed by Celik (2009), which performs principal compo-nent analysis (PCA) and k-means clustering on the pixelsto detect changes in an unsupervised manner. Both algo-rithms perform worse than the proposed method on theHRSCD dataset.

To evaluate the size of the dataset, we have also testedStrategy 4.2 using reduced amounts of data for trainingthe network. The kappa coefficient, in percent, obtainedby using the whole training dataset is 25.49. This value isreduced to 23.34 by using half the training data, and is fur-ther reduced to 22.18 by using a quarter of the data. Thisshows that, as expected, using more data for training thenetwork leads to better results. Nonetheless, it also showsthat the dataset is large enough to allow for even morecomplex and data hungry methods to be trained using theHRSCD dataset in the future.

Finally, it is important to note that the label imperfec-tions in the HRSCD dataset occur not only in the train-ing images, but also in the test images. This means thatthe performance of the proposed methods may be evenhigher than the numbers suggest, since some of the dis-agreements between prediction and ground truth data areactually due to errors in the ground truth data.

5.3. Eppalock lake images

We compare our method in this section to the one pro-posed by Mou et al. (2019), which used recurrent convolu-

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Table 6: Change detection (CD) and land cover mapping (LCM) results of all four of the proposed strategies on the HRSCD dataset. Comparisonwith the methods proposed by El Amin et al. (2016) (Otsu [CNNF-O] and fixed [CNNF-F] thresholding) and by Celik (2009) ([PCA+KM]) areincluded. Results are in percent.

CD LCMKappa Dice Tot. acc. Kappa Tot. acc.

Str. 1 3.99 5.56 86.07 71.92 87.22Str. 2 21.54 - 98.30 - -Str. 3 12.48 13.79 94.72 71.92 87.22

Str. 4.1 19.13 20.23 96.87 67.25 85.74Str. 4.2 25.49 26.33 98.19 71.81 89.01CNNF-O 0.74 2.43 64.54 - -CNNF-F 3.28 4.84 88.66 - -

PCA+KM 0.67 2.31 83.95 - -

Table 7: Change detection results on Eppalock lake test images. Resultsare in percent.

ReCNN-LSTM EF

Bin

ary

CD Tot. acc. 98.67 99.35

Kappa 97.28 98.67No change 98.83 99.47

Change 98.46 99.19

Sem

antic

CD

Tot. acc. 98.70 98.48Kappa 97.52 97.10

No change 98.49 97.73City exp. 84.72 100

Soil change 100 86.07Water change 99.25 99.93

tional neural networks for change detection. In that work,pixels were randomly split into train and test sets. We be-lieve that this split leads to overfitting since neighbouringpixels contain redundant information. This is especiallytrue when using CNNs, which take as inputs patches cen-tred on the considered pixels, meaning the network seesthe same information for training and testing. It is likelythat overfitting takes place, since an accuracy of over 98%is achieved by using only 1000 labelled pixels to train anetwork with 67500 parameters (for their long short-termmemory (LSTM) architecture, which performed the best).The data consists of a single image pair of 631x602 pix-els only partially annotated, with a total of 8895 annotatedpixels which is much less data than what is required fordeep learning methods. The HRSCD dataset presented in

Section 3 contains over 3 million times more labelled pix-els than the Eppalock lake image pair. Despite the flawsof this testing scheme, we have followed it to achieve afair comparison between the methods.

Using the CNN architecture labelled EF by Daudtet al. (2018b), we have achieved excellent numeric resultswhich discouraged the usage of more complex methodswhich would lead to even more extreme overfitting. Theresults achieved by the EF network were better for binarychange detection and equivalent for semantic change de-tection compared to ReCNN-LSTM. The results can beseen in Table 7.

6. Conclusion

The first major contribution presented in this paper isthe first large scale very high resolution semantic changedetection dataset that will be released to the scientificcommunity. This dataset contains 291 pairs of aerial im-ages, together with aligned rasters for change maps andland cover maps. This dataset allows for the first time fordeep learning methods to be used in this context in a fullysupervised manner with minimal concern for overfitting.We have then proposed different methods for using deepFCNNs for semantic change detection. The best amongthe proposed methods is an integrated network that per-forms land cover mapping and change detection simulta-neously, using information from the land cover mappingbranches to help with change detection. We also proposeda sequential training scheme for this network that avoids

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the need of tuning a hyperparameter, which circumventsa costly grid search.

The automatic methods used to generate the HRSCDdataset resulted in noisy labels for both training and test-ing, and how to deal with this problem is still an openquestion. It would also be interesting to explore ways toexplicitly deal with parallax problems which are presentin VHR images which sometimes lead to false positivesdue to the different points of view and the geometry of thescene.

Acknowledgments

This work is part of ONERA’s project DELTA. Wethank X. Zhu and L. Mou (DLR) for the Eppalock Lakeimages.

References

Audebert, N., Le Saux, B., Lefevre, S., 2016. Seman-tic segmentation of earth observation data using multi-modal and multi-scale deep networks, in: Asian Con-ference on Computer Vision, pp. 180–196.

Bazi, Y., Bruzzone, L., Melgani, F., 2005. An unsu-pervised approach based on the generalized gaussianmodel to automatic change detection in multitemporalsar images. IEEE Transactions on Geoscience and Re-mote Sensing 43, 874–887.

Benedek, C., Sziranyi, T., 2009. Change detection in op-tical aerial images by a multilayer conditional mixedmarkov model. IEEE Transactions on Geoscience andRemote Sensing 47, 3416–3430.

Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A.,Torr, P.H., 2016. Fully-convolutional siamese networksfor object tracking, in: European Conference on Com-puter Vision, Springer. pp. 850–865.

Bourdis, N., Denis, M., Sahbi, H., 2011. Constrained op-tical flow for aerial image change detection, in: Inter-national Geoscience and Remote Sensing Symposium,pp. 4176–4179.

Bovolo, F., Bruzzone, L., 2005. A wavelet-based change-detection technique for multitemporal sar images, in:International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, IEEE. pp. 85–89.

Bovolo, F., Bruzzone, L., 2007. A theoretical frameworkfor unsupervised change detection based on changevector analysis in the polar domain. IEEE Transactionson Geoscience and Remote Sensing 45, 218–236.

Bruzzone, L., Bovolo, F., 2013. A novel framework forthe design of change-detection systems for very-high-resolution remote sensing images. Proceedings of theIEEE 101, 609–630.

Bruzzone, L., Prieto, D.F., 2000. Automatic analysis ofthe difference image for unsupervised change detec-tion. IEEE Transactions on Geoscience and RemoteSensing 38, 1171–1182.

Celik, T., 2009. Unsupervised change detection in satel-lite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sens-ing Letters 6, 772–776.

Chen, K., Weinmann, M., Sun, X., Yan, M., Hinz, S.,Jutzi, B., Weinmann, M., 2018a. Semantic segmenta-tion of aerial imagery via multi-scale shuffling convo-lutional neural networks with deep supervision. ISPRSAnnals of Photogrammetry, Remote Sensing & SpatialInformation Sciences 4.

Chen, Y., Ouyang, X., Agam, G., 2018b. MFCNET: End-to-end approach for change detection in images, in:IEEE International Conference on Image Processing,IEEE. pp. 4008–4012.

Chopra, S., Hadsell, R., LeCun, Y., 2005. Learning a sim-ilarity metric discriminatively, with application to faceverification, in: IEEE Conference on Computer Visionand Pattern Recognition, pp. 539–546.

Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lam-bin, E., 2004. Digital change detection methods inecosystem monitoring: A review. International Journalof Remote Sensing 25, 1565–1596.

Dai, X., Khorram, S., 1999. Remotely sensed change de-tection based on artificial neural networks. Photogram-metric engineering and remote sensing 65, 1187–1194.

14

Daudt, R.C., Le Saux, B., Boulch, A., 2018a. Fullyconvolutional siamese networks for change detection,in: International Conference on Image Processing, pp.4063–4067.

Daudt, R.C., Le Saux, B., Boulch, A., Gousseau, Y.,2018b. Urban change detection for multispectral earthobservation using convolutional neural networks, in:International Geoscience and Remote Sensing Sympo-sium, pp. 2119–2122.

Demir, I., Koperski, K., Lindenbaum, D., Pang, G.,Huang, J., Basu, S., Hughes, F., Tuia, D., Raskar, R.,2018. Deepglobe 2018: A challenge to parse the earththrough satellite images. CoRR abs/1805.06561. URL:http://arxiv.org/abs/1805.06561.

El Amin, A.M., Liu, Q., Wang, Y., 2016. Convolu-tional neural network features based change detectionin satellite images, in: First International Workshop onPattern Recognition, International Society for Opticsand Photonics. p. 100110W.

El Amin, A.M., Liu, Q., Wang, Y., 2017. Zoom out cnnsfeatures for optical remote sensing change detection,in: Int. Conference on Image, Vision and Computing,pp. 812–817.

Gopal, S., Woodcock, C., 1996. Remote sensing of forestchange using artificial neural networks. IEEE Transac-tions on Geoscience and Remote Sensing 34, 398–404.

He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residuallearning for image recognition, in: IEEE Conferenceon Computer Vision and Pattern Recognition, pp. 770–778.

Huang, C., Song, K., Kim, S., Townshend, J.R., Davis, P.,Masek, J.G., Goward, S.N., 2008. Use of a dark ob-ject concept and support vector machines to automateforest cover change analysis. Remote Sensing of Envi-ronment 112, 970–985.

Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D.,2013. Change detection from remotely sensed images:From pixel-based to object-based approaches. ISPRSJournal of Photogrammetry and Remote Sensing 80,91–106.

Lambin, E.F., Strahlers, A.H., 1994. Change-vector anal-ysis in multitemporal space: a tool to detect and catego-rize land-cover change processes using high temporal-resolution satellite data. Remote Sensing of Environ-ment 48, 231–244.

Le Saux, B., Randrianarivo, H., 2013. Urban change de-tection in SAR images by interactive learning, in: Inter-national Geoscience and Remote Sensing Symposium,IEEE. pp. 3990–3993.

LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning.Nature 521, 436.

Liu, G., Gousseau, Y., Tupin, F., 2019. A contrario com-parison of local descriptors for change detection in veryhigh spatial resolution satellite images of urban areas.IEEE Transactions on Geoscience and Remote Sensing.

Liu, J., Gong, M., Qin, K., Zhang, P., 2016. A deepconvolutional coupling network for change detectionbased on heterogeneous optical and radar images. IEEETransactions on Neural Networks and Learning Sys-tems 29, 545–559.

Long, J., Shelhamer, E., Darrell, T., 2015. Fully convo-lutional networks for semantic segmentation, in: IEEEConference on Computer Vision and Pattern Recogni-tion, pp. 3431–3440.

Maggiolo, L., Marcos, D., Moser, G., Tuia, D., 2018.Improving maps from cnns trained with sparse, scrib-bled ground truths using fully connected crfs, in: Inter-national Geoscience and Remote Sensing Symposium,IEEE. pp. 2103–2103.

Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P.,2017. High-resolution image classification with con-volutional networks, in: International Geoscience andRemote Sensing Symposium, IEEE. pp. 5157–5160.

Mnih, V., Hinton, G.E., 2010. Learning to detect roadsin high-resolution aerial images, in: European Confer-ence on Computer Vision, pp. 210–223.

Mou, L., Bruzzone, L., Zhu, X.X., 2019. Learningspectral-spatial-temporal features via a recurrent con-volutional neural network for change detection in mul-

15

tispectral imagery. IEEE Transactions on Geoscienceand Remote Sensing 57, 924–935.

Rolnick, D., Veit, A., Belongie, S.J., Shavit, N., 2017.Deep learning is robust to massive label noise. CoRRabs/1705.10694. URL: http://arxiv.org/abs/

1705.10694.

Ronneberger, O., Fischer, P., Brox, T., 2015. U-net: Con-volutional networks for biomedical image segmenta-tion, in: International Conference on Medical ImageComputing and Computer-Assisted Intervention, pp.234–241.

Rosin, P.L., Ioannidis, E., 2003. Evaluation of global im-age thresholding for change detection. Pattern Recog-nition Letters 24, 2345–2356.

Sesnie, S.E., Gessler, P.E., Finegan, B., Thessler, S., 2008.Integrating landsat tm and srtm-dem derived variableswith decision trees for habitat classification and changedetection in complex neotropical environments. Re-mote Sensing of Environment 112, 2145–2159.

Simonyan, K., Zisserman, A., 2015. Very deep convolu-tional networks for large-scale image recognition, in:International Conference on Learning Representations.

Singh, A., 1989. Review article digital change detectiontechniques using remotely-sensed data. InternationalJournal of Remote Sensing 10, 989–1003.

Stent, S., Gherardi, R., Stenger, B., Cipolla, R., 2015.Detecting change for multi-view, long-term surface in-spection, in: British Machine Vision Conference, pp.127.1–127.12.

Vakalopoulou, M., Karantzalos, K., Komodakis, N., Para-gios, N., 2015. Simultaneous registration and changedetection in multitemporal, very high resolution remotesensing data, in: IEEE Conference on Computer Visionand Pattern Recognition Workshops, pp. 61–69.

Volpi, M., Tuia, D., 2017. Dense semantic labeling of sub-decimeter resolution images with convolutional neuralnetworks. IEEE Transactions on Geoscience and Re-mote Sensing 55, 881–893.

Volpi, M., Tuia, D., Bovolo, F., Kanevski, M., Bruzzone,L., 2013. Supervised change detection in vhr imagesusing contextual information and support vector ma-chines. International Journal of Applied Earth Obser-vation and Geoinformation 20, 77–85.

Volpi, M., Tuia, D., Kanevski, M., Bovolo, F., Bruzzone,L., 2009. Supervised change detection in vhr images:a comparative analysis, in: 2009 IEEE InternationalWorkshop on Machine Learning for Signal Processing,IEEE. pp. 1–6.

Zagoruyko, S., Komodakis, N., 2015. Learning to com-pare image patches via convolutional neural networks,in: IEEE Conference on Computer Vision and PatternRecognition, pp. 4353–4361.

Zhan, Y., Fu, K., Yan, M., Sun, X., Wang, H., Qiu, X.,2017. Change detection based on deep siamese convo-lutional network for optical aerial images. IEEE Geo-science and Remote Sensing Letters 14, 1845–1849.

Zhao, J., Gong, M., Liu, J., Jiao, L., 2014. Deep learn-ing to classify difference image for image change de-tection, in: International Joint Conference on NeuralNetworks, IEEE. pp. 411–417.

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