Title :
Detecting nonlinear mixtures in hyperspectral images
Author :
Altmann, Yoann ; Dobigeon, Nicolas ; Tourneret, Jean-Yves
Author_Institution :
IRIT/INP-ENSEEIHT, Univ. of Toulouse, Toulouse, France
Abstract :
This paper presents a new detector for identifying nonlinear mixtures in hyperspectral images. The proposed detector relies on a nonlinear mixing model that approximates the pixel reflectance as a nonlinear combination of pure spectral components contaminated by an additive white Gaussian noise. The parameters involved in the resulting model are estimated using subgradient-based least squares method. A generalized likelihood ratio test is then proposed to decide whether a given pixel results from the commonly used linear mixing model or from a more general nonlinear mixture. The performance of the detection strategy is evaluated thanks to simulations conducted on synthetic data.
Keywords :
AWGN; gradient methods; hyperspectral imaging; least squares approximations; object detection; statistical testing; additive white Gaussian noise; generalized likelihood ratio test; hyperspectral images; linear mixing model; nonlinear mixing model; nonlinear mixture detection; nonlinear mixture identification; pixel reflectance; subgradient-based least squares method; Detectors; Hyperspectral imaging; Polynomials; Signal processing; Vectors; Hyperspectral images; constrained estimation; nonlinearity detection; post-nonlinear mixing model;
Conference_Titel :
Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2012 4th Workshop on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4799-3405-8
DOI :
10.1109/WHISPERS.2012.6874285