{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:08:02Z","timestamp":1760144882022,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62036006"],"award-info":[{"award-number":["62036006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62036006"],"award-info":[{"award-number":["62036006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Heterogeneous image change detection is a very practical and challenging task because the data in the original image have a large distribution difference and the labeled samples of the remote sensing image are usually very few. In this study, we focus on solving the issue of comparing heterogeneous images without supervision. This paper first designs a self-paced multi-scale joint feature mapper (SMJFM) for the mapping of heterogeneous data to similar feature spaces for comparison and incorporates a self-paced learning strategy to weaken the mapper\u2019s capture of non-consistent information. Then, the difference information in the output of the mapper is evaluated from two perspectives, namely noise robustness and detail preservation effectiveness; then, the change detection problem is modeled as a multi-objective optimization problem. We decompose this multi-objective optimization problem into several scalar optimization subproblems with different weights, and use particle swarm optimization to optimize these subproblems. Finally, the robust evaluation strategy is used to fuse the multi-scale change information to obtain a high-precision binary change map. Compared with previous methods, the proposed SMJFM framework has the following three main advantages: First, the unsupervised design alleviates the dilemma of few labels in remote sensing images. Secondly, the introduction of self-paced learning enhances SMJFM\u2019s capture of the unchanged region mapping relationship between heterogeneous images. Finally, the multi-scale change information fusion strategy enhances the robustness of the framework to outliers in the original data.<\/jats:p>","DOI":"10.3390\/rs16111961","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T03:45:08Z","timestamp":1717040708000},"page":"1961","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-Paced Multi-Scale Joint Feature Mapper for Multi-Objective Change Detection in Heterogeneous Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Ying","family":"Wang","sequence":"first","affiliation":[{"name":"Air Traffic Control and Navigation School, Air Force Engineering University, Xi\u2019an 710038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelin","family":"Dang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Collaborative Intelligence Systems, School of Electronic Engineering, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rennong","family":"Yang","sequence":"additional","affiliation":[{"name":"Air Traffic Control and Navigation School, Air Force Engineering University, Xi\u2019an 710038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Song","sequence":"additional","affiliation":[{"name":"Air Traffic Control and Navigation School, Air Force Engineering University, Xi\u2019an 710038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6294-6761","authenticated-orcid":false,"given":"Hao","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Collaborative Intelligence Systems, School of Electronic Engineering, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maoguo","family":"Gong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Collaborative Intelligence Systems, School of Electronic Engineering, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shi, J., Wu, T., Qin, A.K., Lei, Y., and Joen, G. 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