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Proceedings Paper

Progressive sample processing of band selection for hyperspectral imagery
Author(s): Keng-Hao Liu; Hung-Chang Chien; Shih-Yu Chen
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Paper Abstract

Band selection (BS) is one of the most important topics in hyperspectral image (HSI) processing. The objective of BS is to find a set of representative bands that can represent the whole image with lower inter-band redundancy. Many types of BS algorithms were proposed in the past. However, most of them can be carried on in an off-line manner. It means that they can only be implemented on the pre-collected data. Those off-line based methods are sometime useless for those applications that are timeliness, particular in disaster prevention and target detection. To tackle this issue, a new concept, called progressive sample processing (PSP), was proposed recently. The PSP is an "on-line" framework where the specific type of algorithm can process the currently collected data during the data transmission under band-interleavedby-sample/pixel (BIS/BIP) protocol. This paper proposes an online BS method that integrates a sparse-based BS into PSP framework, called PSP-BS. In PSP-BS, the BS can be carried out by updating BS result recursively pixel by pixel in the same way that a Kalman filter does for updating data information in a recursive fashion. The sparse regression is solved by orthogonal matching pursuit (OMP) algorithm, and the recursive equations of PSP-BS are derived by using matrix decomposition. The experiments conducted on a real hyperspectral image show that the PSP-BS can progressively output the BS status with very low computing time. The convergence of BS results during the transmission can be quickly achieved by using a rearranged pixel transmission sequence. This significant advantage allows BS to be implemented in a real time manner when the HSI data is transmitted pixel by pixel.

Paper Details

Date Published: 10 October 2017
PDF: 8 pages
Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104271L (10 October 2017);
Show Author Affiliations
Keng-Hao Liu, National Sun Yat-Sen Univ. (Taiwan)
Hung-Chang Chien, National Sun Yat-Sen Univ. (Taiwan)
Shih-Yu Chen, National Yunlin Univ. of Science and Technology (Taiwan)

Published in SPIE Proceedings Vol. 10427:
Image and Signal Processing for Remote Sensing XXIII
Lorenzo Bruzzone, Editor(s)

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