TY - GEN
T1 - SPATIALLY ADAPTIVE HYPERSPECTRAL UNMIXING BASED ON SUMS OF 2D GAUSSIANS FOR MODELLING ENDMEMBER FRACTION SURFACES
AU - Kizel, Fadi
AU - Shoshany, Maxim
AU - Netanyahu, Nathan S.
N1 - Publisher Copyright: © 2015 IEEE.
PY - 2015/11/10
Y1 - 2015/11/10
N2 - Performing standard unmixing of a hyperspectral image, while taking into account all of the potential endmembers (EMs) in a pixel, is known to be prone to error. Instead, determining first the set of EMs that actually reside in each pixel, leads to enhanced unmixing results. This important insight for achieving higher unmixing accuracy can be exploited efficiently by extracting relevant spatial information from a given image. In this work, we present a new method for spatially adaptive spectral unmixing, called the Gaussian based spatially adaptive unmixing (GBSAU) method. GBSAU takes advantage of the spatial arrangement of the image pixels and their spectral relations in order to determine an actual subset of EMs per pixel. It is based on spatial localization of the EMs by fitting, for each EM, the parameters of the series of spatial Gaussians whose sum represents the EM's fraction surface over the image.
AB - Performing standard unmixing of a hyperspectral image, while taking into account all of the potential endmembers (EMs) in a pixel, is known to be prone to error. Instead, determining first the set of EMs that actually reside in each pixel, leads to enhanced unmixing results. This important insight for achieving higher unmixing accuracy can be exploited efficiently by extracting relevant spatial information from a given image. In this work, we present a new method for spatially adaptive spectral unmixing, called the Gaussian based spatially adaptive unmixing (GBSAU) method. GBSAU takes advantage of the spatial arrangement of the image pixels and their spectral relations in order to determine an actual subset of EMs per pixel. It is based on spatial localization of the EMs by fitting, for each EM, the parameters of the series of spatial Gaussians whose sum represents the EM's fraction surface over the image.
KW - 2D Gaussian fitting
KW - Spatial endmember localization
KW - Spectral unmixing
UR - http://www.scopus.com/inward/record.url?scp=84962583913&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2015.7326812
DO - 10.1109/IGARSS.2015.7326812
M3 - منشور من مؤتمر
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4440
EP - 4443
BT - Remote Sensing: Understanding the Earth for a Safer World
T2 - IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Y2 - 26 July 2015 through 31 July 2015
ER -