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Proceedings Paper

Sparsity based target detection for compressive spectral imagery
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Paper Abstract

Hyperspectral imagery provides significant information about the spectral characteristics of objects and materials present in a scene. It enables object and feature detection, classification, or identification based on the acquired spectral characteristics. However, it relies on sophisticated acquisition and data processing systems able to acquire, process, store, and transmit hundreds or thousands of image bands from a given area of interest which demands enormous computational resources in terms of storage, computationm, and I/O throughputs. Specialized optical architectures have been developed for the compressed acquisition of spectral images using a reduced set of coded measurements contrary to traditional architectures that need a complete set of measurements of the data cube for image acquisition, dealing with the storage and acquisition limitations. Despite this improvement, if any processing is desired, the image has to be reconstructed by an inverse algorithm in order to be processed, which is also an expensive task. In this paper, a sparsity-based algorithm for target detection in compressed spectral images is presented. Specifically, the target detection model adapts a sparsity-based target detector to work in a compressive domain, modifying the sparse representation basis in the compressive sensing problem by means of over-complete training dictionaries and a wavelet basis representation. Simulations show that the presented method can achieve even better detection results than the state of the art methods.

Paper Details

Date Published: 28 September 2016
PDF: 9 pages
Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997126 (28 September 2016); doi: 10.1117/12.2238030
Show Author Affiliations
David Alberto Boada, Univ. Industrial de Santander (Colombia)
Henry Arguello Fuentes Sr., Univ. Industrial de Santander (Colombia)

Published in SPIE Proceedings Vol. 9971:
Applications of Digital Image Processing XXXIX
Andrew G. Tescher, Editor(s)

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