Noise-Robust Spatial Preprocessing Prior to Endmember Extraction from Hyperspectral Data Gabriel Martín, Maciel Zortea and Antonio Plaza Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura, Cáceres, Spain Contact – URL:

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

Introduction to Spectral Unmixing of Hyperspectral Data 2 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011 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 Band b

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 Same output results Introduction to Spectral Unmixing of Hyperspectral Data 3 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011

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

e1e1e1e1 e3e3e3e3 e2e2e2e2 Band X Band 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 4 IEEE 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 5 IEEE 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 c min = p and c max =2 p Spatial Preprocessing Prior to Endmember Extraction 5 IEEE 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 5 IEEE 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 5 IEEE 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 5 IEEE 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 6 IEEE 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 7 IEEE 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 8 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011

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) – Random noise in different signal-to-noise ratios (SNRs) is added to the scenes Experimental Results with Synthetic and Real Hyperspectral Data 9 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2011), Vancouver, Canada, July 24 – 29, 2011

Synthetic Image Generation Database available online: Experimental Results with Synthetic and Real Hyperspectral Data 10 IEEE 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 11 IEEE 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 12 IEEE 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 13 IEEE 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,

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,

Noise-Robust Spatial Preprocessing Prior to Endmember Extraction from Hyperspectral Data Gabriel Martín, Maciel Zortea and Antonio Plaza Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura, Cáceres, Spain Contact – URL: