Anomaly detection and important bands selection for hyperspectral images via sparse PCA

Abstract

We propose a regularised version of the classical singular value decomposition for simultaneous outliers and associated important bands selection. The contributions are twofold: First, we exploit sequential optimisation techniques in L<inf>0</inf> formulation to obtain sparse solution of classical principal component analysis. Second, we have develop new formulation for the anomaly detection problem where the simultaneous identification of important bands can be performed. Experiments in real and simulated data are included to validate the proposed method.

DOI: 10.1109/WHISPERS.2014.8077604

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Cite this paper

@article{VelascoForero2014AnomalyDA, title={Anomaly detection and important bands selection for hyperspectral images via sparse PCA}, author={Santiago Velasco-Forero and Marcus Chen and Alvina Goh and Sze Kim Pang}, journal={2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)}, year={2014}, pages={1-4} }