Spatial-Contextual Information Utilization Framework for Land Cover Change Detection With Hyperspectral Remote Sensed Images
Land cover change detection (LCCD) using bitemporal remote sensing images is a crucial task for identifying the change areas on the Earth's surface. However, the utilization of hyperspectral remote sensing images (HRSIs) introduces challenges as the detection performance is affected by the spec...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing Jg. 61; S. 1 - 11 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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New York
IEEE
2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0196-2892, 1558-0644 |
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| Abstract | Land cover change detection (LCCD) using bitemporal remote sensing images is a crucial task for identifying the change areas on the Earth's surface. However, the utilization of hyperspectral remote sensing images (HRSIs) introduces challenges as the detection performance is affected by the spectral noise and deducing change detection accuracies. In this work, we concentrated on utilizing spatial-contextual information to improve the change detection performance while using HRSIs. First, a band selection approach is used to minimize the spectral redundancy of HRSIs. Second, an iterative spatial-adaptive filter is proposed to smooth the noise of HRSIs. Thereafter, the change magnitude between bitemporal HRSIs is measured by coupling change vector analysis (CVA) and the adaptive region around each pixel, resulting in a change magnitude image (CMI). Subsequently, the CMI is divided into a binary change detection map by using an Ostu threshold method. The experimental results on three pairs of real HRSIs efficiently demonstrated the feasibility and superiorities of the proposed approach compared with six state-of-the-art methods. For example, the improvement rates are approximately 0.43%-11.83% and 1.05%-15.41% for overall accuracy (OA) and average accuracy (AA), respectively. The code of our proposed approach will be available at: https://github.com/ImgSciGroup/2023-HSICD . |
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| AbstractList | Land cover change detection (LCCD) using bitemporal remote sensing images is a crucial task for identifying the change areas on the Earth’s surface. However, the utilization of hyperspectral remote sensing images (HRSIs) introduces challenges as the detection performance is affected by the spectral noise and deducing change detection accuracies. In this work, we concentrated on utilizing spatial-contextual information to improve the change detection performance while using HRSIs. First, a band selection approach is used to minimize the spectral redundancy of HRSIs. Second, an iterative spatial-adaptive filter is proposed to smooth the noise of HRSIs. Thereafter, the change magnitude between bitemporal HRSIs is measured by coupling change vector analysis (CVA) and the adaptive region around each pixel, resulting in a change magnitude image (CMI). Subsequently, the CMI is divided into a binary change detection map by using an Ostu threshold method. The experimental results on three pairs of real HRSIs efficiently demonstrated the feasibility and superiorities of the proposed approach compared with six state-of-the-art methods. For example, the improvement rates are approximately 0.43%–11.83% and 1.05%–15.41% for overall accuracy (OA) and average accuracy (AA), respectively. The code of our proposed approach will be available at: https://github.com/ImgSciGroup/2023-HSICD . |
| Author | Falco, Nicola Benediktsson, Jon Atli Lei, Tao Lv, Zhiyong Zhang, Ming Sun, Weiwei |
| Author_xml | – sequence: 1 givenname: Zhiyong orcidid: 0000-0003-2595-4794 surname: Lv fullname: Lv, Zhiyong email: Lvzhiyong_fly@hotmail.com organization: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China – sequence: 2 givenname: Ming surname: Zhang fullname: Zhang, Ming email: zhangming_sun@163.com organization: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China – sequence: 3 givenname: Weiwei orcidid: 0000-0003-3399-7858 surname: Sun fullname: Sun, Weiwei email: nbsww@outlook.com organization: Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, China – sequence: 4 givenname: Jon Atli orcidid: 0000-0003-0621-9647 surname: Benediktsson fullname: Benediktsson, Jon Atli email: benedikt@hi.is organization: Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavík, Iceland – sequence: 5 givenname: Tao orcidid: 0000-0002-2104-9298 surname: Lei fullname: Lei, Tao email: leitao@sust.edu.cn organization: School of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi'an, China – sequence: 6 givenname: Nicola orcidid: 0000-0003-3307-6098 surname: Falco fullname: Falco, Nicola email: nicolafalco@lbl.gov organization: Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA |
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| Snippet | Land cover change detection (LCCD) using bitemporal remote sensing images is a crucial task for identifying the change areas on the Earth's surface. However,... Land cover change detection (LCCD) using bitemporal remote sensing images is a crucial task for identifying the change areas on the Earth’s surface. However,... |
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| SubjectTerms | Accuracy Adaptive contextual analysis Adaptive filters Change detection Deep learning Detection Earth surface Feature extraction Hyperspectral imaging hyperspectral remote sensing images (HRSIs) Information processing Iterative methods Land cover land cover change detection (LCCD) Redundancy Remote sensing Sun Training Vector analysis White noise |
| Title | Spatial-Contextual Information Utilization Framework for Land Cover Change Detection With Hyperspectral Remote Sensed Images |
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