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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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 61; pp. 1 - 11
Main Authors: Lv, Zhiyong, Zhang, Ming, Sun, Weiwei, Benediktsson, Jon Atli, Lei, Tao, Falco, Nicola
Format: Journal Article
Language:English
Published: New York IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary: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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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3336791