{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:06:52Z","timestamp":1790140012853,"version":"4.0.1"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Sichuan Province","award":["2023NSFSC0243"],"award-info":[{"award-number":["2023NSFSC0243"]}]},{"DOI":"10.13039\/100010144","name":"Chengdu University of Information Technology","doi-asserted-by":"publisher","award":["KYTZ202261"],"award-info":[{"award-number":["KYTZ202261"]}],"id":[{"id":"10.13039\/100010144","id-type":"DOI","asserted-by":"publisher"}]},{"award":["KYTZ202261"],"award-info":[{"award-number":["KYTZ202261"]}],"id":[{"id":"https:\/\/ror.org\/01yxwrh59","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Remote sensing referring image segmentation faces critical challenges including arbitrary target rotation, drastic scale variation, cluttered complex backgrounds, and large visual-language semantic gaps. Existing mainstream segmentation models adopt fixed-receptive-field backbones, coarse unidirectional cross-modal interaction and static learnable object queries, which easily cause small-object missed detection, blurred boundary segmentation and fragmented predictions. This paper proposes a context-aware referring expression segmentation model for remote sensing to tackle the above limitations. We construct a dual-stream feature extraction backbone with InternImage and CLIP text encoder, and design a semantic prior localization map module to generate spatial heatmaps for spatial inductive bias and improve small-object localization recall. A cross-modal context aggregator performs multi-scale bidirectional visual-text alignment, while a dynamic query initialization strategy and a language-guided Transformer decoder progressively improve target localization and mask refinement. Experiments are conducted on two standard RRSIS benchmarks, RefSegRS and RRSIS-D. On RefSegRS, the proposed model achieves an mIoU of 70.43% and an oIoU of 77.34%, and obtains significant improvements on small vehicles, buildings and slender road markings. On the larger-scale RRSIS-D dataset, our model achieves an mIoU of 63.71% and an oIoU of 73.68%. The proposed model provides a solution for accurate multi-scale target segmentation guided by natural language descriptions in complex remote sensing scenes.<\/jats:p>","DOI":"10.3390\/jimaging12090453","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T08:17:54Z","timestamp":1790065074000},"page":"453","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RS-CARES: Context-Aware Cross-Modal Alignment with Semantic Spatial Prior for Referring Remote Sensing Image Segmentation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7120-3548","authenticated-orcid":false,"given":"Hui","family":"Xiong","sequence":"first","affiliation":[{"name":"School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1409-4738","authenticated-orcid":false,"given":"Wen","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2510-7069","authenticated-orcid":false,"given":"Bing","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8370","DOI":"10.1109\/JSTARS.2023.3335891","article-title":"Deep-learning-based Semantic Segmentation of Remote Sensing Images: A Survey","volume":"17","author":"Huang","year":"2023","journal-title":"IEEE J. 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