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The spectral-spatial joint learning for change detection in multispectral imagery
Zhang, Wuxia1,2; Lu, Xiaoqiang1
Source PublicationRemote Sensing
Contribution Rank1

Change detection is one of the most important applications in the remote sensing domain. More and more attention is focused on deep neural network based change detection methods. However, many deep neural networks based methods did not take both the spectral and spatial information into account. Moreover, the underlying information of fused features is not fully explored. To address the above-mentioned problems, a Spectral-Spatial Joint Learning Network (SSJLN) is proposed. SSJLN contains three parts: spectral-spatial joint representation, feature fusion, and discrimination learning. First, the spectral-spatial joint representation is extracted from the network similar to the Siamese CNN (S-CNN). Second, the above-extracted features are fused to represent the difference information that proves to be effective for the change detection task. Third, the discrimination learning is presented to explore the underlying information of obtained fused features to better represent the discrimination. Moreover, we present a new loss function that considers both the losses of the spectral-spatial joint representation procedure and the discrimination learning procedure. The effectiveness of our proposed SSJLN is verified on four real data sets. Extensive experimental results show that our proposed SSJLN can outperform the other state-of-the-art change detection methods. © 2019 by the authors.

Keywordmultispectral imagery spectral-spatial representation Siamese CNN feature fusion discrimination learning change detection
Indexed BySCI ; EI
WOS IDWOS:000459944400028
PublisherMDPI AG, Postfach, Basel, CH-4005, Switzerland
EI Accession Number20190706505805
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorLu, Xiaoqiang
Affiliation1.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China;
2.University of Chinese Academy of Sciences, Beijing; 100049, China
Recommended Citation
GB/T 7714
Zhang, Wuxia,Lu, Xiaoqiang. The spectral-spatial joint learning for change detection in multispectral imagery[J]. Remote Sensing,2019,11(3).
APA Zhang, Wuxia,&Lu, Xiaoqiang.(2019).The spectral-spatial joint learning for change detection in multispectral imagery.Remote Sensing,11(3).
MLA Zhang, Wuxia,et al."The spectral-spatial joint learning for change detection in multispectral imagery".Remote Sensing 11.3(2019).
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