Noise-Robust Spatial Preprocessing Prior to Endmember Extraction from Hyperspectral Data Gabriel...

Noise-Robust Spatial Preprocessing Prior to

Endmember Extraction from

Hyperspectral Data



Hyperspectral DataGabriel MartínGabriel Martín,, Maciel Zortea Maciel Zortea and and Antonio PlazaAntonio Plaza

Hyperspectral Computing LaboratoryHyperspectral Computing LaboratoryDepartment of Technology of Computers and CommunicationsDepartment of Technology of Computers and Communications

University of Extremadura, Cáceres, SpainUniversity of Extremadura, Cáceres, SpainContact e-mail: [email protected] – URL: http://www.umbc.edu/rssipl/people/aplazaContact e-mail: [email protected] – URL: http://www.umbc.edu/rssipl/people/aplaza

Talk Outline:Talk Outline:1. Introduction to spectral unmixing of hyperspectral data

2. Spatial preprocessing prior to endmember extraction

2.1. Spatial preprocessing (SPP)

2.2. Region-based spatial preprocessing (RBSPP)

2.3. Noise-robust spatial preprocessing (NRSPP)

3. Experimental results

3.1. Synthetic hyperspectral data

3.2. Real hyperspectral data over the Cuprite mining district, Nevada

4. Conclusions and future research lines

1. Introduction to spectral unmixing of hyperspectral data









Noise-Robust Spatial Preprocessing for Endmember Extraction

IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011

Presence of mixed pixels in hyperspectral data

Pure pixel(water)

Mixed pixel(soil + rocks)

Mixed pixel(vegetation + soil)

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Some particularities of hyperspectral data not to be found in other remote sensing data:

• Mixed pixels (due to insufficient spatial resolution and mixing effects in surfaces)

• Intimate mixtures (happen at particle level; increasing spatial resolution does not address them)

Introduction to Spectral Unmixing of Hyperspectral Data

1IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011



Linear interaction

)y,x(yx,)y,x( nMf )y,x(yx,)y,x( nMf

Linear spectral unmixing (LSU)

• The goal is to find extreme pixel vectors (endmembers) that can be used to unmix other mixed pixels in the data using a linear mixture model

• Each mixed pixel can be obtained as a combination of endmember fractional abundances; a crucial issue is how to find the endmembers

Band a

Ban

d b

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Using spatial information in endmember extraction • Much effort has been given to extracting endmembers in

spectral terms

• Endmember extraction does not generally include information about spatial context

• There is a need to incorporate the spatial correlation of features in the unmixing process

• We develop a new strategy to include spatial information in endmember extraction

• The method works as a pre-processing module (easy to combine with available methods)

Pixel spatial coor-dinates randomly

shuffled

Endmember extraction Endmember extractionSame output results



Spatial Pre-Processing (SPP)

Developed by Zortea and Plaza (IEEE Trans. Geosci. Remote Sens., 2009)

1. Move a spatial kernel around each hyperspectral pixel vector and calculate a spatial correction factor for each pixel

2. Assign a weight to the spectral signature of each pixel depending on the spectral similarity between each pixel and its spatial neighbors, so that anomalous pixels are displaced to the centroid, while spatially homogeneous pixels are not displaced

Spatial Preprocessing Prior to Endmember Extraction


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Band XBand X

Ban

d Y

Ban

d Y

Spatial Pre-Processing (SPP)

Developed by Zortea and Plaza (IEEE Trans. Geosci. Remote Sens., 2009)

1. Move a spatial kernel around each hyperspectral pixel vector and calculate a spatial correction factor for each pixel

2. Assign a weight to the spectral signature of each pixel depending on the spectral similarity between each pixel and its spatial neighbors, so that anomalous pixels are displaced from the centroid, while spatially homogeneous pixels are not displaced

3. Apply spectral-based endmember extraction (using, e.g., OSP, VCA or N-FINDR) after the preprocessing, obtaining a final set of endmembers from the original image



Estimation of the number of

endmembers p

Hyperspectral image with n

spectral bands

Several possibilities: Chang’s VD; Bioucas’ HySime; Luo and

Chanussot’s eigenvalue approach



Region-Based Spatial Pre-Processing (RBSPP)

Developed by Martín and Plaza (IEEE Geosci. Remote Sens. Lett., 2011)


spectral bands


endmembers p

Unsupervised clustering

ISODATA is used to partition the original image into c clusters, where

cmin=p and cmax=2p





Morphological erosion and

redundant region thinning


spectral bands


endmembers p


Intended to remove mixed pixels at the region borders; multidimensional morphological operators are used to

accomplish this task





Region selection using orthogonal

projections


spectral bands


endmembers p




An orthogonal subspace projection approach is then applied to the mean spectra of the regions to retain a final set of p regions





Preprocessing module


spectral bands


endmembers p




Region selection using orthogonal

projections

Automatic endmember

extraction and unmixing

p fully cons-trained

abun-dance maps (one

per endmember

)





Noise-robust spatial preprocessing (NRSPP)

• The method first derives a spatial homogeneity index which is relatively insensitive to the noise present in the original hyperspectral data; then, it fuses this index with a spectral-based classification, obtaining a set of pure regions which are used to guide the endmember searching process

• Step 1: Apply multidimensional Gaussian filtering using different scales, which results in different filtered versions of the original hyperspectral image




• Step 2: Calculate the root mean square error (RMSE) between the original image and each of the filtered images and derive a spatial homogeneity index as the average of the obtained difference values; such spatial homogeneity calculation is robust in the presence of noise




• Step 3: Perform a spectral-based unsupervised classification of the original image; here, we use the ISODATA algorithm, where the number of components retained was set to p, the number of endmembers

• Step 4: For each cluster in the classification map, a percentage (alpha) of spatially homogeneous pixels are selected; then, we apply the OSP algorithm over the averaged signatures in each resulting region to select the most highly pure regions (removing those which contain mixed pixels)

• Endmember extraction is finally applied to the pixels retained after the NRSPP, which acts as a pre-processing module (as the SPP and RBSPP)



Synthetic Image Generation

• The scenes have been generated using fractals to generate random spatial patterns

• Each fractal image is divided into a set of classes or clusters

• Mixed pixels are generated inside each cluster using library signatures

• Spectral signatures obtained from a library of mineral spectral signatures available online from U.S. Geological Survey (USGS) – http://speclab.cr.usgs.gov

• Random noise in different signal-to-noise ratios (SNRs) is added to the scenes

Experimental Results with Synthetic and Real Hyperspectral Data


Synthetic Image Generation

• Database available online: http://www.umbc.edu/rssipl/people/aplaza/fractals.zip



Experiments with Synthetic Images

• Average spectral angle (degrees) between ground-truth USGS spectra and the endmembers extracted across five synthetic scenes with different SNRs (alpha=70)

• RMSE after reconstructing the five synthetic scenes (with different SNRs) using the endmembers extracted by OSP (alpha=70)



AVIRIS Data Over Cuprite, Nevada


12IEEE International Geoscience and Remote Sensing Symposium (IGARSS’2011), Vancouver, Canada, July 24 – 29, 2011

Experiments with the AVIRIS Cuprite hyperspectral image

OSP (81 seconds) AMEE (96 seconds) SSEE (320 seconds)

SPP+OSP (49+81 seconds)

RBSPP+OSP (78+14 seconds)

NRSPP+OSP (71+12 seconds)


13IEEE International Geoscience and Remote Sensing Symposium (IGARSS’2011), Vancouver, Canada, July 24 – 29, 2011

RMSE=0.165

RMSE=0.265

RMSE=0.101

RMSE=0.067

RMSE=0.085

RMSE=0.129

Times measured

in Intel Core i7 920 CPU at 2.67 GHz with 4 GB OF RAM

(p = 22)

Conclusions and Future Lines.-

• We have developed a new spatial pre-processing method which can be used prior to endmember extraction and spectral unmixing of hyperspectral images

• The proposed method shows some advantages over other existing approaches, in particular, when the noise level in the hyperspectral data is relatively high

• The results obtained with synthetic scenes anticipate that the incorporation of spatial information may be beneficial in order to allow a better modelling of spatial patterns and robustness in the presence of noise

• The results obtained with real scenes indicate that the incorporation of spatial information directs the endmember searching process to spatially homogeneous regions in the original hyperspectral scene

• Future work will be directed towards comparisons with multiple endmember spectral mixture analysis techniques (comparable in terms of abundance estimation accuracy but more complex in computational terms)

Conclusions and Hints at Plausible Future Research

IEEE International Geoscience and Remote Sensing Symposium (IGARSS’09), Cape Town, South Africa, July 12 – 17, 2009 14

IEEE J-STARS Special Issue on Hyperspectral Image and Signal Processing

IEEE International Geoscience and Remote Sensing Symposium (IGARSS’09), Cape Town, South Africa, July 12 – 17, 2009 15



Hyperspectral Data



Hyperspectral DataGabriel MartínGabriel Martín,, Maciel Zortea Maciel Zortea and and Antonio PlazaAntonio Plaza

Hyperspectral Computing LaboratoryHyperspectral Computing LaboratoryDepartment of Technology of Computers and CommunicationsDepartment of Technology of Computers and Communications

University of Extremadura, Cáceres, SpainUniversity of Extremadura, Cáceres, SpainContact e-mail: [email protected] – URL: http://www.umbc.edu/rssipl/people/aplazaContact e-mail: [email protected] – URL: http://www.umbc.edu/rssipl/people/aplaza

Noise-Robust Spatial Preprocessing Prior to Endmember Extraction from Hyperspectral Data Gabriel...

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