乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T04:26:53Z","timestamp":1727065613227},"reference-count":72,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T00:00:00Z","timestamp":1636070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&D Program of China","award":["2019YFC1510905","4192034"]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62125102"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have provided a better perspective for modeling long-range context in images, but usually suffer from high computational complexity and memory usage. In this paper, we explored the potential of using transformers for efficient building extraction. We design an efficient dual-pathway transformer structure that learns the long-term dependency of tokens in both their spatial and channel dimensions and achieves state-of-the-art accuracy on benchmark building extraction datasets. Since single buildings in remote sensing images usually only occupy a very small part of the image pixels, we represent buildings as a set of \u201csparse\u201d feature vectors in their feature space by introducing a new module called \u201csparse token sampler\u201d. With such a design, the computational complexity in transformers can be greatly reduced over an order of magnitude. We refer to our method as Sparse Token Transformers (STT). Experiments conducted on the Wuhan University Aerial Building Dataset (WHU) and the Inria Aerial Image Labeling Dataset (INRIA) suggest the effectiveness and efficiency of our method. Compared with some widely used segmentation methods and some state-of-the-art building extraction methods, STT has achieved the best performance with low time cost.<\/jats:p>","DOI":"10.3390\/rs13214441","type":"journal-article","created":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T02:25:54Z","timestamp":1636079154000},"page":"4441","source":"Crossref","is-referenced-by-count":86,"title":["Building Extraction from Remote Sensing Images with Sparse Token Transformers"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0483-1306","authenticated-orcid":false,"given":"Keyan","family":"Chen","sequence":"first","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"Beijing Key Laboratory of Digital Media, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1774-552X","authenticated-orcid":false,"given":"Zhengxia","family":"Zou","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA"}]},{"given":"Zhenwei","family":"Shi","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"Beijing Key Laboratory of Digital Media, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guo, M., Liu, H., Xu, Y., and Huang, Y. 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