Slow Feature Analysis for Change Detection in Multispectral Imagery

Change detection was one of the earliest and is also one of the most important applications of remote sensing technology. For multispectral images, an effective solution for the change detection problem is to exploit all the available spectral bands to detect the spectral changes. However, in practi...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 52; číslo 5; s. 2858 - 2874
Hlavní autori:	Wu, Chen, Du, Bo, Zhang, Liangpei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY IEEE 01.05.2014 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Applied geophysics Change detection Change detection algorithms Covariance matrices Detection algorithms Earth sciences Earth, ocean, space Eigenvalues and eigenfunctions Exact sciences and technology Feature extraction Image detection image transformation Internal geophysics Invariants Pixels Principal component analysis Remote sensing slow feature analysis (SFA) Spectra Spectral bands image transformation Change detection slow feature analysis (SFA) experimental studies algorithms Thematic Mapper imagery transformations remote sensing technology Pixel
ISSN:	0196-2892, 1558-0644
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Abstract	Change detection was one of the earliest and is also one of the most important applications of remote sensing technology. For multispectral images, an effective solution for the change detection problem is to exploit all the available spectral bands to detect the spectral changes. However, in practice, the temporal spectral variance makes it difficult to separate changes and nonchanges. In this paper, we propose a novel slow feature analysis (SFA) algorithm for change detection. Compared with changed pixels, the unchanged ones should be spectrally invariant and varying slowly across the multitemporal images. SFA extracts the most temporally invariant component from the multitemporal images to transform the data into a new feature space. In this feature space, the differences in the unchanged pixels are suppressed so that the changed pixels can be better separated. Three SFA change detection approaches, comprising unsupervised SFA, supervised SFA, and iterative SFA, are constructed. Experiments on two groups of real Enhanced Thematic Mapper data sets show that our proposed method performs better in detecting changes than the other state-of-the-art change detection methods.
AbstractList	Change detection was one of the earliest and is also one of the most important applications of remote sensing technology. For multispectral images, an effective solution for the change detection problem is to exploit all the available spectral bands to detect the spectral changes. However, in practice, the temporal spectral variance makes it difficult to separate changes and nonchanges. In this paper, we propose a novel slow feature analysis (SFA) algorithm for change detection. Compared with changed pixels, the unchanged ones should be spectrally invariant and varying slowly across the multitemporal images. SFA extracts the most temporally invariant component from the multitemporal images to transform the data into a new feature space. In this feature space, the differences in the unchanged pixels are suppressed so that the changed pixels can be better separated. Three SFA change detection approaches, comprising unsupervised SFA, supervised SFA, and iterative SFA, are constructed. Experiments on two groups of real Enhanced Thematic Mapper data sets show that our proposed method performs better in detecting changes than the other state-of-the-art change detection methods.
Author	Du, Bo Zhang, Liangpei Wu, Chen
Author_xml	– sequence: 1 givenname: Chen surname: Wu fullname: Wu, Chen email: chen.wu@ whu.edu.cn organization: State Key Lab. of Inf. Eng. in Surveying, Mapping, & Remote Sensing, Wuhan Univ., Wuhan, China – sequence: 2 givenname: Bo surname: Du fullname: Du, Bo email: remoteking@whu.edu.cn organization: Sch. of Comput. Sci., Wuhan Univ., Wuhan, China – sequence: 3 givenname: Liangpei surname: Zhang fullname: Zhang, Liangpei email: zlp62@whu.edu.cn organization: State Key Lab. of Inf. Eng. in Surveying, Mapping, & Remote Sensing, Wuhan Univ., Wuhan, China
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PublicationTitle	IEEE transactions on geoscience and remote sensing
PublicationTitleAbbrev	TGRS
PublicationYear	2014
Publisher	IEEE Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Snippet	Change detection was one of the earliest and is also one of the most important applications of remote sensing technology. For multispectral images, an...
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SubjectTerms	Applied geophysics Change detection Change detection algorithms Covariance matrices Detection algorithms Earth sciences Earth, ocean, space Eigenvalues and eigenfunctions Exact sciences and technology Feature extraction Image detection image transformation Internal geophysics Invariants Pixels Principal component analysis Remote sensing slow feature analysis (SFA) Spectra Spectral bands
Title	Slow Feature Analysis for Change Detection in Multispectral Imagery
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Volume	52
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