LAND COVER CHANGE DETECTION USING RADARSAT-2 POLARIMETRIC SAR IMAGES

Zhixin Qi and Anthony Gar-On YehThe University of Hong Kong, Hong Kong, China

Introduction1

Study area and data2

Methodology3

Results and discussion4

Conclusions5

Outline

Background

There are many illegal land developments in some of China’s rapidly developing regions, such as the Pearl River Delta (PRD).

RADAR vs. Optical remote sensing

Radar remote sensing, which is not affected by cloud conditions, is promising for monitoring short-term land cover changes.

Multi-polarization vs. single-polarization

Single-polarization SAR

HH

Polarimetric SAR (PolSAR)

VH

VVHH

HV

polarization

The polarization information contained in the waves backscattered from a given medium is highly related to:

• its geometrical structure reflectivity, shape and orientation• its geophysical properties such as humidity, roughness, …

H

V or H

H

H

Completely Polarised Scattering Partially Polarised Scattering

Study Objective

Research questions

• Unsupervised methods

– They cannot determine types of changes.

• Post-classification comparison (PCC)

– Poor accuracy of PolSAR image classification caused by the limited spectral information and speckle noise

• Pixel-based methods

– They may cause false alarms due to the speckle effect.

Study objective

• This study aims to develop a new method that integrates change vector analysis (CVA) and post-classification comparison (PCC) with object-oriented image analysis (OOIA) to detect land cover changes from RADARSAT-2 polarimetric SAR (PolSAR) images.

Study Area

Land Cover Classes in the Study Area

Study Data

• RADARSAT-2 Fine Quad-Pol images (Single Look Complex).

• Full polarization: HH, HV, VH and VV.

• Incidence angle: 31.50°.

Field Work

Field Work

 Class Sub-class  Plots Pixels

Change Barren land to crop/natural vegetation 68 28,995

  Water to crop/natural vegetation 47 19,089

  Crop/natural vegetation to water 75 17,738

  Barren land to built-up areas 51 14,257

  Barren land to water 41 9,417

  Water to lawns 7 4,089

  Crop/natural vegetation to barren land 10 3,392

  Water to barren land 9 3,380

  Total 308 100,357

No change Banana 107 41,656

  Barren land 84 23,743

  Forests 118 36,437

  Lawns 98 34,159

  Crop/natural vegetation 202 93,196

  Built-up areas 224 65,939

  Water 130 66,130

  Total 963 361,260

Number of change and no-change samples selected for the verification of land cover change detection results

Methodology

Land cover change detection using two PolSAR images acquired over the same area at different times

• Polarimetric decomposition can be used to support the classification of PolSAR data. It is aimed at extracting polarimetric parameters related to the physical scattering mechanisms of the observed objects.

• In this study, PolSARPro_4.03 software package was used to implement polarimetric decomposition.

Polarimetric Decomposition

Polarimetric Decomposition

Polarimetric Decomposition

Decomposition Methods

• Pauli (Cloude & Pottier, 1996)• Barnes (Barnes, 1988)• Huynen (Huynen, 1970)• Cloude (Cloude, 1985)• Holm (Holm & Barnes, 1988)• H/A/Alpha (Cloude & Pottier, 1997)• Freeman 2 Components (Freeman, 2007)

• Freeman 3 Components (Freeman & Durden, 1998)

• Van Zyl (Van Zyl, 1993)• Neumann (Neumann et al., 2009)• Krogager (Krogager, 1990)• Yamaguchi (Yamaguchi et al., 2005)• Touzi (Touzi, 2007) methods

Image Segmentation

Determining the optimal scale for the segmentation of the Pauli RGB composition image of RADARSAT-2 PolSAR data.

Separate Segmentation

Hierarchical Segmentation

Hierarchical segmentation for delineating image objects from two successive RADARSAT-2 PolSAR images

A feature is an attribute that represents certain information concerning objects of interest, such as color, shape, and texture.

Change Vector Analysis (CVA)

Two images, image (t1) and image (t2), are acquired over the same area at different times t1 and t2. If k features are extracted from an image object, the feature vectors of the image object in the two images are given by X = (x1, x2, …, xk)T and Y = (y1, y2, …, yk)T respectively, the feature change vectors are defined as

where G includes all the change information between the two images for a given image object, and the change magnitude is computed with

The higher the is, the more likely that changes take place. Unsupervised classifiers or threshold methods are commonly applied on the change magnitude to identify changes.

kk yx

yx

yx

YXG

22

11

G

2222

211 )()()( kk yxyxyxG

G

(1)

(2)

Change Vector Analysis (CVA)

March 21, 2009 September 29,2009

Change magnitude Changed areas

PolSAR image classification

Methodology of land cover classification using RADARSAT-2 PolSAR images

Proposed Method Vs. WSC

Classification Results

Land Cover Change Results

Land cover change detection results (a)Proposed method (CVA, PCC, and OOIA)(b)WSC-based PCC(c)PCC and OOIA (without CVA)(d)CVA and PCC (without OOIA)(e)CVA and OOIA (without PCC)

e

Accuracy AssessmentChange type Accuracy

statisticsProposed method(CVA, PCC, and OOIA)

WSC-based PCC CVA and OOIA (without PCC)

PCC and OOIA(without CVA)

CVA and PCC(without OOIA)

All the types DA (%) 86.71 94.94 90.51 93.15 88.13

FAR (%) 3.35 38.10 7.57 17.87 10.25

OER (%) 5.51 30.92 7.99 15.48 10.60

BL-CN DA (%) 46.50 33.23 NA 58.08 31.23

FAR (%) 0.11 0.50 NA 0.18 0.30

OER (%) 3.46 4.66 NA 2.80 4.59

BL-BU DA (%) 46.59 32.98 NA 48.93 45.46

FAR (%) 0.09 0.33 NA 0.09 0.87

OER (%) 1.73 2.39 NA 1.66 2.53

BL-W DA (%) 47.97 41.89 NA 48.32 44.88

FAR (%) 0.09 0.79 NA 0.20 0.40

OER (%) 1.15 1.96 NA 1.25 1.52

CN-BL DA (%) 73.88 34.08 NA 76.62 38.41

FAR (%) 0.85 0.85 NA 0.94 0.60

OER (%) 1.04 1.58 NA 1.11 1.05

CN-W DA (%) 52.06 37.38 NA 52.06 40.27

FAR (%) 0.05 0.27 NA 0.05 0.31

OER (%) 1.89 2.67 NA 1.89 2.59

W-BL DA (%) 68.76 51.30 NA 68.91 68.05

FAR (%) 0.86 1.33 NA 1.26 1.62

OER (%) 1.09 1.68 NA 1.48 1.85

W-L DA (%) 89.90 71.14 NA 89.90 47.79

FAR (%) 0.51 1.05 NA 0.51 0.60

OER (%) 0.59 1.29 NA 0.59 1.05

W-CN DA (%) 63.87 39.22 NA 63.87 38.40

FAR (%) 0.11 0.20 NA 0.11 0.16

OER (%) 1.60 2.70 NA 1.60 2.70

Conclusions

• The proposed method performs much better than WSC-based PCC in term of land cover change detection using RADARSAT-2 PolSAR images.

• The use of CVA before PCC can significantly reduce false alarms caused by the error of the classification of PolSAR images.

• Using PCC after CVA can reduce false alarms caused by environmental changes, such as seasonal vegetation growth and moisture variation. PCC that is based on the proposed classification approach, which integrates polarimetric decomposition, decision tree algorithms, and SVMs, achieves much higher accuracy than WSC-based PCC.

• OOIA reduces false alarms caused by speckles in PolSAR images and improves the accuracy of change type determination.

• Further investigation will be conducted to examine the effect of seasonal vegetation growth on the monitoring of human-induced land cover changes as well as how to distinguish between human-induced land cover changes and changes caused by seasonal vegetation growth.

Conclusions

• The proposed method performs much better than WSC-based PCC in term of land cover change detection using RADARSAT-2 PolSAR images.

• The use of CVA before PCC can significantly reduce false alarms caused by the error of the classification of PolSAR images.

• Using PCC after CVA can reduce false alarms caused by environmental changes, such as seasonal vegetation growth and moisture variation. PCC that is based on the proposed classification approach, which integrates polarimetric decomposition, decision tree algorithms, and SVMs, achieves much higher accuracy than WSC-based PCC.

• OOIA reduces false alarms caused by speckles in PolSAR images and improves the accuracy of change type determination.

• Further investigation will be conducted to examine the effect of seasonal vegetation growth on the monitoring of human-induced land cover changes as well as how to distinguish between human-induced land cover changes and changes caused by seasonal vegetation growth.