期刊论文

Peng, FF

英文

Remote Sensing

2072-4292

2023

15

18

4555-

10.3390/rs15184555

Conceptualization, L.J. and L.H. (Li Huang); methodology, L.H. (Li Huang), L.J. and F.P.; validation, F.L. and F.P.; formal analysis, F.L.; investigation, F.L., L.H. (Li Huang) and F.P.; writing—original draft preparation, L.J., L.H. (Li Huang), F.L. and F.P.; visualization, F.L. and L.H. (Li Huang); writing—review and editing, L.J., F.P. and L.H. (Lei Hu); funding acquisition, L.J. and L.H. (Lei Hu). All authors have read and agreed to the published version of the manuscript. This research was supported by the National Key Research and Development Program of China (No. 2020YFC1512003) and the National Natural Science Foundation of China (No. 41901315 and No. 42071389). L.J. was also supported by the Fundamental Research Funds for the Central Universities (WUT:223108001).

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Semantic change detection (SCD) holds a critical place in remote sensing image interpretation, as it aims to locate changing regions and identify their associated land cover classes. Presently, post-classification techniques stand as the predominant strategy for SCD due to their simplicity and efficacy. However, these methods often overlook the intricate relationships between alterations in land cover. In this paper, we argue that comprehending the interplay of changes within land cover maps holds the key to enhancing SCD's performance. With this insight, a Temporal-Transform Module (TTM) is d...