{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T17:37:12Z","timestamp":1769017032060,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA28110502"],"award-info":[{"award-number":["XDA28110502"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["41871236"],"award-info":[{"award-number":["41871236"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["41971193"],"award-info":[{"award-number":["41971193"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["20200403174SF"],"award-info":[{"award-number":["20200403174SF"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["20200403187SF"],"award-info":[{"award-number":["20200403187SF"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["JJKH20210667KJ"],"award-info":[{"award-number":["JJKH20210667KJ"]}]},{"name":"National Natural Science Foundation of China","award":["XDA28110502"],"award-info":[{"award-number":["XDA28110502"]}]},{"name":"National Natural Science Foundation of China","award":["41871236"],"award-info":[{"award-number":["41871236"]}]},{"name":"National Natural Science Foundation of China","award":["41971193"],"award-info":[{"award-number":["41971193"]}]},{"name":"National Natural Science Foundation of China","award":["20200403174SF"],"award-info":[{"award-number":["20200403174SF"]}]},{"name":"National Natural Science Foundation of China","award":["20200403187SF"],"award-info":[{"award-number":["20200403187SF"]}]},{"name":"National Natural Science Foundation of China","award":["JJKH20210667KJ"],"award-info":[{"award-number":["JJKH20210667KJ"]}]},{"name":"Foundation of the Jilin Provincial Science &amp; Technology Department","award":["XDA28110502"],"award-info":[{"award-number":["XDA28110502"]}]},{"name":"Foundation of the Jilin Provincial Science &amp; Technology Department","award":["41871236"],"award-info":[{"award-number":["41871236"]}]},{"name":"Foundation of the Jilin Provincial Science &amp; Technology Department","award":["41971193"],"award-info":[{"award-number":["41971193"]}]},{"name":"Foundation of the Jilin Provincial Science &amp; Technology Department","award":["20200403174SF"],"award-info":[{"award-number":["20200403174SF"]}]},{"name":"Foundation of the Jilin Provincial Science &amp; Technology Department","award":["20200403187SF"],"award-info":[{"award-number":["20200403187SF"]}]},{"name":"Foundation of the Jilin Provincial Science &amp; Technology Department","award":["JJKH20210667KJ"],"award-info":[{"award-number":["JJKH20210667KJ"]}]},{"name":"Foundation of the Jilin Province Education Department","award":["XDA28110502"],"award-info":[{"award-number":["XDA28110502"]}]},{"name":"Foundation of the Jilin Province Education Department","award":["41871236"],"award-info":[{"award-number":["41871236"]}]},{"name":"Foundation of the Jilin Province Education Department","award":["41971193"],"award-info":[{"award-number":["41971193"]}]},{"name":"Foundation of the Jilin Province Education Department","award":["20200403174SF"],"award-info":[{"award-number":["20200403174SF"]}]},{"name":"Foundation of the Jilin Province Education Department","award":["20200403187SF"],"award-info":[{"award-number":["20200403187SF"]}]},{"name":"Foundation of the Jilin Province Education Department","award":["JJKH20210667KJ"],"award-info":[{"award-number":["JJKH20210667KJ"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Since the unsupervised segmentation of high-resolution remote sensing is a highly challenging task, the introduction of deep learning and processing may be a sensible choice to improve the quality of unsupervised segmentation. Unfortunately, any attempt to direct using unsupervised deep neural networks (UDNNs) to perform this task will be hindered by many obstacles: uncontrollable refinement processes, excessive fragmentation at the borders and excessive computing resource requirements. These obstacles can prevent us from obtaining acceptable results. To address this problem, this article proposes a hierarchical object-focused and grid-based deep unsupervised segmentation method for high-resolution remote sensing images (HOFG). Based on a grid approach, HOFG first adopt a lazy deep segmentation method (LDSM) to handle fragmentation and large image sizes. Then, a hierarchical and iterative segmentation strategy is introduced to reduce the accuracy expectation for the LDSM by means of a cascaded focus mechanism, making the entire segmentation process more controllable. HOFG can overcome all of the above obstacles while utilizing the high recognition ability of UDNNs. In experiments, HOFG are compared with shallow and deep unsupervised segmentation methods. The results show that HOFG can obtain fewer segments while maintaining a high accuracy. HOFG transform the unsupervised classification ability of UDNNs into a controllable and stable segmentation ability, making HOFG valuable for practical applications. The results show that on average, HOFG need only 81.73% as many segments as traditional shallow methods to achieve a high overall accuracy, and HOFG can obtain a 7.2% higher accuracy than a UDNN even when using only approximately 18% as many segments. HOFG can effectively and controllably utilize the recognition ability of UDNNs to achieve better unsupervised segmentation results.<\/jats:p>","DOI":"10.3390\/rs14225768","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T02:36:36Z","timestamp":1668566196000},"page":"5768","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Hierarchical Object-Focused and Grid-Based Deep Unsupervised Segmentation Method for High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Xin","family":"Pan","sequence":"first","affiliation":[{"name":"School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, China"},{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology, Changchun Institute of Technology, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[{"name":"Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology, Changchun Institute of Technology, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7302-8042","authenticated-orcid":false,"given":"Xiaofeng","family":"Li","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5892","DOI":"10.1080\/01431161.2019.1584687","article-title":"An object-based and heterogeneous segment filter convolutional neural network for high-resolution remote sensing image classification","volume":"40","author":"Pan","year":"2019","journal-title":"Int. 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