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Automatic SAR Change Detection Based on Visual Saliency and Multi-Hierarchical Fuzzy Clustering

IEEE journal of selected topics in applied earth observations and remote sensing, 2022, Vol.15, p.7755-7769 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 ;ISSN: 1939-1404 ;EISSN: 2151-1535 ;DOI: 10.1109/JSTARS.2022.3199017 ;CODEN: IJSTHZ

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Title:
Automatic SAR Change Detection Based on Visual Saliency and Multi-Hierarchical Fuzzy Clustering
Author: Peng, Yao ; Cui, Bin ; Yin, Hujun ; Zhang, Yonghong ; Du, Peijun
Subjects: Adaptive sampling ; Adaptive systems ; Algorithms ; Artificial neural networks ; Change detection ; Change detection algorithms ; classed-balanced adaptive focal loss ; Cluster analysis ; Clustering ; Clustering algorithms ; Deep learning ; Detection ; Feature extraction ; fuzzy clustering ; Machine learning ; Neural networks ; Radar imaging ; Radar polarimetry ; Salience ; SAR (radar) ; Synthetic aperture radar ; synthetic aperture radar (SAR) ; visual saliency difference map ; Visualization
Is Part Of: IEEE journal of selected topics in applied earth observations and remote sensing, 2022, Vol.15, p.7755-7769
Description: Change detection based on multi-temporal synthetic aperture radar (SAR) images plays a significant role in environmental and earth observations. With the advancement in deep neural networks, existing work in the literature mainly concentrates on developing self-supervised methods to generate pseudo-labeled samples to guide the subsequent deep learning based detection. However, this way of selecting sample inevitably introduces erroneous labels and imbalance between unchanged and changed classes, thus causing deterioration in change detection performance. To mitigate these issues, we have proposed a SAR change detection network based on visual saliency and multi-hierarchical fuzzy clustering. Specifically, with multi-dimensional difference feature representations, a visual saliency based difference map is constructed for accurate difference feature extraction. By integrating neighborhood information and hierarchical clustering, the multi-hierarchical fuzzy local information C-means clustering algorithm has been developed to identify potential changed regions for sample selection. A class-balanced adaptive focal loss has further been incorporated into the network training to obtain accurate predictions. Extensive experiments and comparisons on five datasets have been performed. The proposed method has achieved averaged accuracy of 99.07% and Kappa coefficient of 79.87%, outperforming other state-of-the-art algorithms both visually and quantitatively.
Publisher: Piscataway: IEEE
Language: English
Identifier: ISSN: 1939-1404
EISSN: 2151-1535
DOI: 10.1109/JSTARS.2022.3199017
CODEN: IJSTHZ
Source: IEEE Open Access Journals
DOAJ Directory of Open Access Journals

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Automatic SAR Change Detection Based on Visual Saliency and Multi-Hierarchical Fuzzy Clustering

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022 ;ISSN: 1939-1404 ;EISSN: 2151-1535 ;DOI: 10.1109/JSTARS.2022.3199017 ;CODEN: IJSTHZ

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