Abstract

A classification method of compressed hyperspectral images acquired by using a Coded Aperture Snapshot Spectral Imaging (CASSI) system is proposed. The CASSI system captures spectral imaging information of a 3-dimensional cube with just a single 2-dimensional measurement containing the coded and spectrally dispersed source field. The proposed method is based on the concept that each pixel in the hyperspectral image lies in a low-dimensional subspace, and thus it can be represented as a sparse linear combination of vectors in a dictionary which is obtained from training samples. The method incorporates interpixel correlation within the image by assuming a sparse multidimensional representation of the scene. The recovered sparse vector is then used directly to determine the class label of the test pixel. The proposed algorithm is used to classify real hyperspectral data cubes directly from their CASSI measurements.a

© 2012 Optical Society of America

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