Jacobi Angles for Simultaneous Diagonalization
SIAM Journal on Matrix Analysis and Applications
A fast fixed-point algorithm for independent component analysis
Neural Computation
ICA Mixture Model based Unsupervised Classification of Hyperspectral Imagery
AIPR '02 Proceedings of the 31st Applied Image Pattern Recognition Workshop on From Color to Hyperspectral: Advancements in Spectral Imagery Exploitation
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Separation of statistically dependent sources using an L2-distance non-Gaussianity measure
Signal Processing - Special section: Distributed source coding
Unsupervised image classification, segmentation, and enhancement using ICA mixture models
IEEE Transactions on Image Processing
Independent component analysis based on nonparametric density estimation
IEEE Transactions on Neural Networks
Soft clustering for nonparametric probability density function estimation
Pattern Recognition Letters
Maximum likelihood blind image separation using nonsymmetrical half-plane Markov random fields
IEEE Transactions on Image Processing
Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Maximum contrast analysis for nonnegative blind source separation
Computers & Mathematics with Applications
Hybrid computational methods for hyperspectral image analysis
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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We approach the estimation of material percentages per pixel (endmember fractional abundances) in hyperspectral remote-sensed images as a blind source separation problem. This task is commonly known as spectral unmixing. Classical techniques require the knowledge of the existing materials and their spectra, which is an unrealistic situation in most cases. In contrast to recently presented blind techniques based on independent component analysis, we implement here a dependent component analysis strategy, namely the MaxNG (maximum non-Gaussianity) algorithm, which is capable to separate even strongly dependent signals. We prove that, when the abundances verify a separability condition, they can be extracted by searching the local maxima of non-Gaussianity. We also provide enough theoretical as well as experimental facts that indicate that this condition holds true for endmember abundances. In addition, we discuss the implementation of MaxNG in a noisy scenario, we introduce a new technique for the removal of scale ambiguities of estimated sources, and a new fast algorithm for the calculation of a Parzen windows-based NG measure. We compare MaxNG to commonly used independent component analysis algorithms, such as FastICA and JADE. We analyze the efficiency of MaxNG in terms of the number of sensor channels, the number of available samples and other factors, by testing it on synthetically generated as well as real data. Finally, we present some examples of application of our technique to real images captured by the MIVIS airborne imaging spectrometer. Our results show that MaxNG is a good tool for spectral unmixing in a blind scenario.