{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:14:58Z","timestamp":1777126498211,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"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":["42201300"],"award-info":[{"award-number":["42201300"]}],"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":["LGN22C130016"],"award-info":[{"award-number":["LGN22C130016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Basic Public Welfare Research Project of China","award":["42201300"],"award-info":[{"award-number":["42201300"]}]},{"name":"Zhejiang Provincial Basic Public Welfare Research Project of China","award":["LGN22C130016"],"award-info":[{"award-number":["LGN22C130016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semantic segmentation forms the foundation for understanding very high resolution (VHR) remote sensing images, with extensive demand and practical application value. The convolutional neural networks (CNNs), known for their prowess in hierarchical feature representation, have dominated the field of semantic image segmentation. Recently, hierarchical vision transformers such as Swin have also shown excellent performance for semantic segmentation tasks. However, the hierarchical structure enlarges the receptive field to accumulate features and inevitably leads to the blurring of object boundaries. We introduce a novel object-aware network, Embedding deep SuperPixel, for VHR image semantic segmentation called ESPNet, which integrates advanced ConvNeXt and the learnable superpixel algorithm. Specifically, the developed task-oriented superpixel generation module can refine the results of the semantic segmentation branch by preserving object boundaries. This study reveals the capability of utilizing deep convolutional neural networks to accomplish both superpixel generation and semantic segmentation of VHR images within an integrated end-to-end framework. The proposed method achieved mIoU scores of 84.32, 90.13, and 55.73 on the Vaihingen, Potsdam, and LoveDA datasets, respectively. These results indicate that our model surpasses the current advanced methods, thus demonstrating the effectiveness of the proposed scheme.<\/jats:p>","DOI":"10.3390\/rs16203805","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T07:47:05Z","timestamp":1728892025000},"page":"3805","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Object-Aware Network Embedding Deep Superpixel for Semantic Segmentation of Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2426-6236","authenticated-orcid":false,"given":"Ziran","family":"Ye","sequence":"first","affiliation":[{"name":"Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Spatial Planning and Design, Hangzhou City University, Hangzhou 310015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baiyu","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4671-0131","authenticated-orcid":false,"given":"Xiangfeng","family":"Tan","sequence":"additional","affiliation":[{"name":"Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengdi","family":"Dai","sequence":"additional","affiliation":[{"name":"Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dedong","family":"Kong","sequence":"additional","affiliation":[{"name":"Institute of Digital Agriculture, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. 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