{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:23:12Z","timestamp":1772119392571,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T00:00:00Z","timestamp":1715040000000},"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":["41901410"],"award-info":[{"award-number":["41901410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023AH050103"],"award-info":[{"award-number":["2023AH050103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Research Project of Anhui Educational Committee","award":["41901410"],"award-info":[{"award-number":["41901410"]}]},{"name":"Natural Science Research Project of Anhui Educational Committee","award":["2023AH050103"],"award-info":[{"award-number":["2023AH050103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Effectively and efficiently retrieving images from remote-sensing databases is a critical challenge in the realm of remote-sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the potential of multi-level feature integration from sketches remains underexplored, leading to suboptimal retrieval performance. To address this gap, our study introduces a novel zero-shot, sketch-based retrieval method for remote-sensing images, leveraging multi-level feature extraction, self-attention-guided tokenization and filtering, and cross-modality attention update. This approach employs only vision information and does not require semantic knowledge concerning the sketch and image. It starts by employing multi-level self-attention guided feature extraction to tokenize the query sketches, as well as self-attention feature extraction to tokenize the candidate images. It then employs cross-attention mechanisms to establish token correspondence between these two modalities, facilitating the computation of sketch-to-image similarity. Our method significantly outperforms existing sketch-based remote-sensing image retrieval techniques, as evidenced by tests on multiple datasets. Notably, it also exhibits robust zero-shot learning capabilities in handling unseen categories and strong domain adaptation capabilities in handling unseen novel remote-sensing data. The method\u2019s scalability can be further enhanced by the pre-calculation of retrieval tokens for all candidate images in a database. This research underscores the significant potential of multi-level, attention-guided tokenization in cross-modal remote-sensing image retrieval. For broader accessibility and research facilitation, we have made the code and dataset used in this study publicly available online.<\/jats:p>","DOI":"10.3390\/rs16101653","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T09:03:38Z","timestamp":1715072618000},"page":"1653","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Zero-Shot Sketch-Based Remote-Sensing Image Retrieval Based on Multi-Level and Attention-Guided Tokenization"],"prefix":"10.3390","volume":"16","author":[{"given":"Bo","family":"Yang","sequence":"first","affiliation":[{"name":"Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China"},{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9371-4867","authenticated-orcid":false,"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China"},{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1354-8035","authenticated-orcid":false,"given":"Xiaoshuang","family":"Ma","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China"},{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beiping","family":"Song","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China"},{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuang","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanghai Ubiquitous Navigation Technology Co., Ltd., Shanghai 201702, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangde","family":"Sun","sequence":"additional","affiliation":[{"name":"The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.inffus.2020.10.008","article-title":"Image retrieval from remote sensing big data: A survey","volume":"67","author":"Li","year":"2021","journal-title":"Inf. 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