Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images
AbstractThis 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
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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.
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 Sensing. 2017; 9(10):1008.Chicago/Turabian Style
Liu, Sicong; Du, Qian; Tong, Xiaohua; Samat, Alim; Pan, Haiyan; Ma, Xiaolong. 2017. "Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images." Remote Sens. 9, no. 10: 1008.
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