{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T08:00:04Z","timestamp":1764403204489,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"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":["62002083","61971153","62071136","OFSLRSS202210","LH2021F012","3072021CFT0801","3072022QBZ0805","3072022CF0808"],"award-info":[{"award-number":["62002083","61971153","62071136","OFSLRSS202210","LH2021F012","3072021CFT0801","3072022QBZ0805","3072022CF0808"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Fund of State Key Laboratory of Remote Sensing Science","award":["62002083","61971153","62071136","OFSLRSS202210","LH2021F012","3072021CFT0801","3072022QBZ0805","3072022CF0808"],"award-info":[{"award-number":["62002083","61971153","62071136","OFSLRSS202210","LH2021F012","3072021CFT0801","3072022QBZ0805","3072022CF0808"]}]},{"name":"Heilongjiang Provincial Natural Science Foundation of China","award":["62002083","61971153","62071136","OFSLRSS202210","LH2021F012","3072021CFT0801","3072022QBZ0805","3072022CF0808"],"award-info":[{"award-number":["62002083","61971153","62071136","OFSLRSS202210","LH2021F012","3072021CFT0801","3072022QBZ0805","3072022CF0808"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62002083","61971153","62071136","OFSLRSS202210","LH2021F012","3072021CFT0801","3072022QBZ0805","3072022CF0808"],"award-info":[{"award-number":["62002083","61971153","62071136","OFSLRSS202210","LH2021F012","3072021CFT0801","3072022QBZ0805","3072022CF0808"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multispectral image change detection is an important application in the field of remote sensing. Multispectral images usually contain many complex scenes, such as ground objects with diverse scales and proportions, so the change detection task expects the feature extractor is superior in adaptive multi-scale feature learning. To address the above-mentioned problems, a multispectral image change detection method based on multi-scale adaptive kernel network and multimodal conditional random field (MSAK-Net-MCRF) is proposed. The multi-scale adaptive kernel network (MSAK-Net) extends the encoding path of the U-Net, and designs a weight-sharing bilateral encoding path, which simultaneously extracts independent features of bi-temporal multispectral images without introducing additional parameters. A selective convolution kernel block (SCKB) that can adaptively assign weights is designed and embedded in the encoding path of MSAK-Net to extract multi-scale features in images. MSAK-Net retains the skip connections in the U-Net, and embeds an upsampling module (UM) based on the attention mechanism in the decoding path, which can give the feature map a better expression of change information in both the channel dimension and the spatial dimension. Finally, the multimodal conditional random field (MCRF) is used to smooth the detection results of the MSAK-Net. Experimental results on two public multispectral datasets indicate the effectiveness and robustness of the proposed method when compared with other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs14215368","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T09:59:37Z","timestamp":1666778377000},"page":"5368","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Change Detection Method Based on Multi-Scale Adaptive Convolution Kernel Network and Multimodal Conditional Random Field for Multi-Temporal Multispectral Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7308-9590","authenticated-orcid":false,"given":"Shou","family":"Feng","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Yuanze","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Yingjie","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Hao","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Chunhui","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7449-7469","authenticated-orcid":false,"given":"Yaoxuan","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, National University of Defense Technology, Hefei 230031, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0545-3450","authenticated-orcid":false,"given":"Chunhua","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Control Engineering, Naval Aviation University Qingdao Campus, Qingdao 266041, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5751","DOI":"10.1109\/TGRS.2019.2901945","article-title":"A deep learning method for change detection in synthetic aperture radar images","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. 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