remote sensing images, and analyzed existing open issues and challenges. In particular, we focused on the spectral–spatial perspective to find robust solutions to the important multiclass CD problem. Accordingly, two approaches were proposed, including M2C2VA and SPC2VA. By taking advantage of the spectral and spatial joint analysis on the multispectral change representation, the original pixel-level CD performance was enhanced by considering both the spectral variation at the global scale and the spectral homogeneity and spatial connectivity and regularity of change targets at the local scale. Experimental results obtained two real multispectral datasets covering a complex urban scenario, and a large-scale tsunami disaster scenario confirmed the effectiveness of the proposed approaches in terms of higher CD accuracy and computational efficiency when compared with the reference methods. For future works, advanced techniques still need to be designed to deal with more complex real unsupervised CD cases, mainly focusing on, but not limited to the open issues and challenges pointed out in section 1.2.2.
1.7. Acknowledgements
This work was supported by the Natural Science Foundation of China under Grant 42071324, 41601354, and by the Shanghai Rising-Star Program (21QA1409100).
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