noise-robust spatial preprocessing prior to endmember extraction from hyperspectral data gabriel...
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Noise-Robust Spatial Preprocessing Prior to
Endmember Extraction from
Hyperspectral Data
Noise-Robust Spatial Preprocessing Prior to
Endmember Extraction from
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
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
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
Introduction to Spectral Unmixing of Hyperspectral Data
2IEEE 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
1e
2e
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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
Introduction to Spectral Unmixing of Hyperspectral Data
3IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
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
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
Noise-Robust Spatial Preprocessing for Endmember Extraction
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
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
4IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
ee11
ee33
ee22
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
Spatial Preprocessing Prior to Endmember Extraction
4IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
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
Spatial Preprocessing Prior to Endmember Extraction
5IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Region-Based Spatial Pre-Processing (RBSPP)
Developed by Martín and Plaza (IEEE Geosci. Remote Sens. Lett., 2011)
Hyperspectral image with n
spectral bands
Estimation of the number of
endmembers p
Unsupervised clustering
ISODATA is used to partition the original image into c clusters, where
cmin=p and cmax=2p
Spatial Preprocessing Prior to Endmember Extraction
5IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Region-Based Spatial Pre-Processing (RBSPP)
Developed by Martín and Plaza (IEEE Geosci. Remote Sens. Lett., 2011)
Morphological erosion and
redundant region thinning
Hyperspectral image with n
spectral bands
Estimation of the number of
endmembers p
Unsupervised clustering
Intended to remove mixed pixels at the region borders; multidimensional morphological operators are used to
accomplish this task
Spatial Preprocessing Prior to Endmember Extraction
5IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Region-Based Spatial Pre-Processing (RBSPP)
Developed by Martín and Plaza (IEEE Geosci. Remote Sens. Lett., 2011)
Region selection using orthogonal
projections
Hyperspectral image with n
spectral bands
Estimation of the number of
endmembers p
Unsupervised clustering
Morphological erosion and
redundant region thinning
An orthogonal subspace projection approach is then applied to the mean spectra of the regions to retain a final set of p regions
Spatial Preprocessing Prior to Endmember Extraction
5IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Region-Based Spatial Pre-Processing (RBSPP)
Developed by Martín and Plaza (IEEE Geosci. Remote Sens. Lett., 2011)
Preprocessing module
Hyperspectral image with n
spectral bands
Estimation of the number of
endmembers p
Unsupervised clustering
Morphological erosion and
redundant region thinning
Region selection using orthogonal
projections
Automatic endmember
extraction and unmixing
p fully cons-trained
abun-dance maps (one
per endmember
)
Spatial Preprocessing Prior to Endmember Extraction
5IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Region-Based Spatial Pre-Processing (RBSPP)
Developed by Martín and Plaza (IEEE Geosci. Remote Sens. Lett., 2011)
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
Spatial Preprocessing Prior to Endmember Extraction
6IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Noise-robust spatial preprocessing (NRSPP)
• 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
Spatial Preprocessing Prior to Endmember Extraction
7IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Noise-robust spatial preprocessing (NRSPP)
• 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)
Spatial Preprocessing Prior to Endmember Extraction
8IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
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
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
Noise-Robust Spatial Preprocessing for Endmember Extraction
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
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
9IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
Synthetic Image Generation
• Database available online: http://www.umbc.edu/rssipl/people/aplaza/fractals.zip
Experimental Results with Synthetic and Real Hyperspectral Data
10IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
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)
Experimental Results with Synthetic and Real Hyperspectral Data
11IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011
AVIRIS Data Over Cuprite, Nevada
Experimental Results with Synthetic and Real Hyperspectral Data
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)
Experimental Results with Synthetic and Real Hyperspectral Data
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
Noise-Robust Spatial Preprocessing Prior to
Endmember Extraction from
Hyperspectral Data
Noise-Robust Spatial Preprocessing Prior to
Endmember Extraction from
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