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

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...

Full description

Bibliographic Details
Main Authors: Sicong Liu, Qian Du, Xiaohua Tong, Alim Samat, Haiyan Pan, Xiaolong Ma
Format: Article
Language:English
Published: MDPI AG 2017-09-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/9/10/1008
id doaj-de56669793b94a6a8881bd92cfba6bd8
record_format Article
spelling doaj-de56669793b94a6a8881bd92cfba6bd82020-11-25T00:09:01ZengMDPI AGRemote Sensing2072-42922017-09-01910100810.3390/rs9101008rs9101008Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral ImagesSicong Liu0Qian Du1Xiaohua Tong2Alim Samat3Haiyan Pan4Xiaolong Ma5College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geoinformatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geoinformatics, Tongji University, Shanghai 200092, ChinaXinjiang Institute of Ecology and Geography, CAS and the CAS Research Center for Ecology and Environment of Central Asia, Urumqi 830011, ChinaCollege of Surveying and Geoinformatics, Tongji University, Shanghai 200092, ChinaCollege of Surveying and Geoinformatics, Tongji University, Shanghai 200092, ChinaThis 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.https://www.mdpi.com/2072-4292/9/10/1008change detection (CD)hyperspectral imagesdimensionality reductionband selectionmulti-temporal imagesremote sensing
collection DOAJ
language English
format Article
sources DOAJ
author Sicong Liu
Qian Du
Xiaohua Tong
Alim Samat
Haiyan Pan
Xiaolong Ma
spellingShingle Sicong Liu
Qian Du
Xiaohua Tong
Alim Samat
Haiyan Pan
Xiaolong Ma
Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images
Remote Sensing
change detection (CD)
hyperspectral images
dimensionality reduction
band selection
multi-temporal images
remote sensing
author_facet Sicong Liu
Qian Du
Xiaohua Tong
Alim Samat
Haiyan Pan
Xiaolong Ma
author_sort Sicong Liu
title Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images
title_short Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images
title_full Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images
title_fullStr Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images
title_full_unstemmed Band Selection-Based Dimensionality Reduction for Change Detection in Multi-Temporal Hyperspectral Images
title_sort band selection-based dimensionality reduction for change detection in multi-temporal hyperspectral images
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2017-09-01
description 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.
topic change detection (CD)
hyperspectral images
dimensionality reduction
band selection
multi-temporal images
remote sensing
url https://www.mdpi.com/2072-4292/9/10/1008
work_keys_str_mv AT sicongliu bandselectionbaseddimensionalityreductionforchangedetectioninmultitemporalhyperspectralimages
AT qiandu bandselectionbaseddimensionalityreductionforchangedetectioninmultitemporalhyperspectralimages
AT xiaohuatong bandselectionbaseddimensionalityreductionforchangedetectioninmultitemporalhyperspectralimages
AT alimsamat bandselectionbaseddimensionalityreductionforchangedetectioninmultitemporalhyperspectralimages
AT haiyanpan bandselectionbaseddimensionalityreductionforchangedetectioninmultitemporalhyperspectralimages
AT xiaolongma bandselectionbaseddimensionalityreductionforchangedetectioninmultitemporalhyperspectralimages
_version_ 1725413375919259648