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
Hauptverfasser: Lv, Zhiyong, Zhang, Ming, Sun, Weiwei, Benediktsson, Jon Atli, Lei, Tao, Falco, Nicola
Format: Journal Article
Sprache:Englisch
Veröffentlicht: 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 .
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
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  orcidid: 0000-0003-2595-4794
  surname: Lv
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  organization: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
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  givenname: Ming
  surname: Zhang
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  email: zhangming_sun@163.com
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  orcidid: 0000-0003-3399-7858
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  fullname: Sun, Weiwei
  email: nbsww@outlook.com
  organization: Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, China
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  givenname: Jon Atli
  orcidid: 0000-0003-0621-9647
  surname: Benediktsson
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  surname: Lei
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  organization: School of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi'an, China
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  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
URI https://ieeexplore.ieee.org/document/10332250
https://www.proquest.com/docview/2899204570
Volume 61
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