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Remote Sens. 2017, 9(10), 1008; doi:10.3390/rs9101008

Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images

1
College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China
2
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
3
Xinjiang Institute of Ecology and Geography, CAS and the CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, China
*
Authors to whom correspondence should be addressed.
Received: 31 August 2017 / Revised: 23 September 2017 / Accepted: 26 September 2017 / Published: 29 September 2017
(This article belongs to the Section Remote Sensing Image Processing)
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Abstract

This paper proposes to use band selection-based dimensionality reduction (BS-DR) technique in addressing a challenging multi-temporal hyperspectral images change detection (HSI-CD) problem. The aim of this work is to analyze and evaluate in detail the CD performance by selecting the most informative band subset from the original high-dimensional data space. In particular, for cases where ground reference data are available or unavailable, either supervised or unsupervised CD approaches are designed. The following sub-problems in HSI-CD are investigated, including: (1) the estimated number of multi-class changes; (2) the binary CD; (3) the multiple CD; (4) the estimated optimal number of selected bands; and (5) computational efficiency. The main contribution of this paper is to provide for the first time a thorough analysis of the impacts of band selection on the HSI-CD problem, thus to fix the gap in the state-of-the-art techniques either by simply utilizing the full dimensionality of the data or exploring a complex hierarchical change analysis. It is applicable to CD problems in multispectral or PolSAR images when the feature space is expanded for discriminant feature extraction. Two real multi-temporal hyperspectral Hyperion datasets are used to validate the proposed approaches. Quantitative and qualitative experimental results demonstrated that by selecting a subset of the most informative and distinct spectral bands, the proposed approaches offered better CD performance than the state-of-the-art techniques using original full bands, without losing the change representative and discriminable capabilities of a detector. View Full-Text
Keywords: change detection (CD); hyperspectral images; dimensionality reduction; band selection; multi-temporal images; remote sensing change detection (CD); hyperspectral images; dimensionality reduction; band selection; multi-temporal images; remote sensing
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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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MDPI and ACS Style

Liu, S.; Du, Q.; Tong, X.; Samat, A.; Pan, H.; Ma, X. Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images. Remote Sens. 2017, 9, 1008.

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