{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:09:20Z","timestamp":1769551760471,"version":"3.49.0"},"reference-count":71,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T00:00:00Z","timestamp":1699056000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shaanxi Provincial Department of Science and Technology Fund Project \u201cShaanxi Provincial Innovation Capability Support Program\u201d","award":["2021PT-009"],"award-info":[{"award-number":["2021PT-009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the rapid development and promotion of deep learning technology in the field of remote sensing, building change detection (BCD) has made great progress. Some recent approaches have improved detailed information about buildings by introducing high-frequency information. However, there are currently few methods considering the effect of other frequencies in the frequency domain for enhancing feature representation. To overcome this problem, we propose a multi-scale discrete cosine transform (DCT) network (MDNet) with U-shaped architecture, which is composed of two novel DCT-based modules, i.e., the dual-dimension DCT attention module (D3AM) and multi-scale DCT pyramid (MDP). The D3AM aims to employ the DCT to obtain frequency information from both spatial and channel dimensions for refining building feature representation. Furthermore, the proposed MDP can excavate multi-scale frequency information and construct a feature pyramid through multi-scale DCT, which can elevate multi-scale feature extraction of ground targets with various scales. The proposed MDNet was evaluated with three widely used BCD datasets (WHU-CD, LEVIR-CD, and Google), demonstrating that our approach can achieve more convincing results compared to other comparative methods. Moreover, extensive ablation experiments also present the effectiveness of our proposed D3AM and MDP.<\/jats:p>","DOI":"10.3390\/rs15215243","type":"journal-article","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T14:51:01Z","timestamp":1699109461000},"page":"5243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Multi-Scale Discrete Cosine Transform Network for Building Change Detection in Very-High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Yangpeng","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Economics and Management, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}]},{"given":"Lijuan","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}]},{"given":"Qianyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}]},{"given":"Jing","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1976","DOI":"10.1109\/JPROC.2022.3219376","article-title":"Land cover change detection with heterogeneous remote sensing images: Review, progress, and perspective","volume":"110","author":"Lv","year":"2022","journal-title":"Proc. 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