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Remote Sens. 2016, 8(3), 238; doi:10.3390/rs8030238

A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

1
Faculty of Architectural Engineering, Civil Engineering and Environment, Ningbo University, Ningbo 315211, China
2
Qidong Photoelectric Remote Sensing Center, Shanghai Institute of Technical Physics of the Chinese Academy of Sciences, Qidong 226200, China
3
Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China
*
Author to whom correspondence should be addressed.
Academic Editors: Lenio Soares Galvao, Xiaofeng Li and Prasad S. Thenkabail
Received: 19 November 2015 / Revised: 8 January 2016 / Accepted: 25 January 2016 / Published: 15 March 2016
View Full-Text   |   Download PDF [2048 KB, uploaded 15 March 2016]   |  

Abstract

A novel Symmetric Sparse Representation (SSR) method has been presented to solve the band selection problem in hyperspectral imagery (HSI) classification. The method assumes that the selected bands and the original HSI bands are sparsely represented by each other, i.e., symmetrically represented. The method formulates band selection into a famous problem of archetypal analysis and selects the representative bands by finding the archetypes in the minimal convex hull containing the HSI band points (i.e., one band corresponds to a band point in the high-dimensional feature space). Without any other parameter tuning work except the size of band subset, the SSR optimizes the band selection program using the block-coordinate descent scheme. Four state-of-the-art methods are utilized to make comparisons with the SSR on the Indian Pines and PaviaU HSI datasets. Experimental results illustrate that SSR outperforms all four methods in classification accuracies (i.e., Average Classification Accuracy (ACA) and Overall Classification Accuracy (OCA)) and three quantitative evaluation results (i.e., Average Information Entropy (AIE), Average Correlation Coefficient (ACC) and Average Relative Entropy (ARE)), whereas it takes the second shortest computational time. Therefore, the proposed SSR is a good alternative method for band selection of HSI classification in realistic applications. View Full-Text
Keywords: symmetric sparse representation; band selection; hyperspectral imagery; classification; archetypal analysis symmetric sparse representation; band selection; hyperspectral imagery; classification; archetypal analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sun, W.; Jiang, M.; Li, W.; Liu, Y. A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification. Remote Sens. 2016, 8, 238.

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