Image Regression With Structure Cycle Consistency for Heterogeneous Change Detection

Change detection (CD) between heterogeneous images is an increasingly interesting topic in remote sensing. The different imaging mechanisms lead to the failure of homogeneous CD methods on heterogeneous images. To address this challenge, we propose a structure cycle consistency-based image regressio...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2024-02, Vol.35 (2), p.1613-1627
Main Authors: Sun, Yuli, Lei, Lin, Guan, Dongdong, Wu, Junzheng, Kuang, Gangyao, Liu, Li
Format: Article
Language:English
Subjects:
Adaptive graph (AG)
Change detection
Change detection algorithms
Consistency
cycle consistency
Feature extraction
Fractals
heterogeneous
image regression
Learning systems
multimodal
Radar polarimetry
Regression
Regularization
Remote sensing
Source code
Training
Transformations (mathematics)
unsupervised change detection (CD)
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Summary:Change detection (CD) between heterogeneous images is an increasingly interesting topic in remote sensing. The different imaging mechanisms lead to the failure of homogeneous CD methods on heterogeneous images. To address this challenge, we propose a structure cycle consistency-based image regression method, which consists of two components: the exploration of structure representation and the structure-based regression. We first construct a similarity relationship-based graph to capture the structure information of image; here, a k -selection strategy and an adaptive-weighted distance metric are employed to connect each node with its truly similar neighbors. Then, we conduct the structure-based regression with this adaptively learned graph. More specifically, we transform one image to the domain of the other image via the structure cycle consistency, which yields three types of constraints: forward transformation term, cycle transformation term, and sparse regularization term. Noteworthy, it is not a traditional pixel value-based image regression, but an image structure regression, i.e., it requires the transformed image to have the same structure as the original image. Finally, change extraction can be achieved accurately by directly comparing the transformed and original images. Experiments conducted on different real datasets show the excellent performance of the proposed method. The source code of the proposed method will be made available at https://github.com/yulisun/AGSCC .
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2022.3184414