Adaptive Local Structure Consistency-Based Heterogeneous Remote Sensing Change Detection

Change detection (CD) of heterogeneous remote sensing images is a challenging topic, which plays an important role in natural disaster emergency response. Due to the different imaging mechanisms of heterogeneous sensors, it is hard to directly compare the images. To address this challenge, we explor...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Lei, Lin, Sun, Yuli, Kuang, Gangyao
Format: Article
Language:English
Subjects:
Adaptive local structure
Atmospheric measurements
Change detection
Consistency
Detection
Disaster management
Domains
Emergency preparedness
Emergency response
Fractals
graph
Graphical representations
heterogeneous remote sensing
Imaging techniques
Natural disasters
Optical imaging
Optical sensors
Radar polarimetry
Remote sensing
Sun
unsupervised change detection (CD)
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Summary:Change detection (CD) of heterogeneous remote sensing images is a challenging topic, which plays an important role in natural disaster emergency response. Due to the different imaging mechanisms of heterogeneous sensors, it is hard to directly compare the images. To address this challenge, we explore an unsupervised CD method based on adaptive local structure consistency (ALSC) between heterogeneous images in this letter, which constructs an adaptive graph representing the local structure for each patch in one image domain and then projects this graph to the other image domain to measure the change level. This local structure consistency exploits the fact that the heterogeneous images share the same structure information for the same ground object, which is imaging modality-invariant. To avoid heterogeneous data confusion, the pixelwise change image is calculated in the same image domain by graph projection. By comparing with some state-of-the-art methods, the experimental results show the effectiveness of the proposed ALSC-based CD method.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2020.3037930