{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:21:42Z","timestamp":1781533302581,"version":"3.54.5"},"reference-count":34,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T00:00:00Z","timestamp":1777334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Technical Services for Intelligent Identification of Land Categories in Guangxi","award":["2026SC500"],"award-info":[{"award-number":["2026SC500"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62262011"],"award-info":[{"award-number":["62262011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["62262011"],"award-info":[{"award-number":["62262011"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Guangxi Major S&T Special Program","award":["GuikeAA23062035-2"],"award-info":[{"award-number":["GuikeAA23062035-2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The Swin Transformer exhibits limitations in fine-grained land use and land cover (LULC) classification, particularly in capturing high-frequency texture details and representing low-contrast regions. To address these issues, we propose a novel network model, termed LACE-Net, which integrates local frequency-domain energy and adaptive contrast enhancement. Built upon the Swin Transformer backbone, the model introduces an innovative Local Frequency-Domain Energy-Adaptive Contrast Enhancement Multi-Scale Attention (LACE). This block consists of parallel branches for frequency-domain perception and contrast enhancement, which effectively combine texture and illumination physical priors. In addition, a texture-adaptive momentum adjustment mechanism is incorporated to refine the spatial enhancement attention weights dynamically. Consequently, LACE-Net greatly strengthens the modeling and representation of high-frequency details and complex spatial structural features. Experiments are performed on a self-constructed Guangxi regional dataset (denoted as GLC-30) and the publicly available remote sensing scene classification benchmark dataset NWPU-RESISC45. The results show that LACE-Net achieves a Top-1 accuracy (Top-1 Acc) of 96.48% and a macro-averaged F1 score (mF1) of 93.13%. These results outperform current mainstream vision models, particularly in mitigating the spectral confusion issue of \u201csame spectrum, different objects.\u201d The model exhibits superior fine-grained classification performance and robust generalization across datasets.<\/jats:p>","DOI":"10.3390\/computers15050281","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T09:55:39Z","timestamp":1777370139000},"page":"281","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LACE-Net: A Swin Transformer with Local Frequency-Domain Energy and Adaptive Contrast Enhancement for Fine-Grained Land Cover Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2440-0670","authenticated-orcid":false,"given":"Yongmei","family":"Tan","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5844-7493","authenticated-orcid":false,"given":"Gong","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China"},{"name":"Technology Innovation Center for Natural Resources Monitoring and Evaluation of Beibu Gulf Economic Zone, Ministry of Natural Resources, Nanning 530219, China"},{"name":"Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9026-5494","authenticated-orcid":false,"given":"Yan","family":"Huang","sequence":"additional","affiliation":[{"name":"Technology Innovation Center for Natural Resources Monitoring and Evaluation of Beibu Gulf Economic Zone, Ministry of Natural Resources, Nanning 530219, China"},{"name":"Guangxi Institute of Natural Resources Survey and Monitoring, Nanning 530201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6646-8747","authenticated-orcid":false,"given":"Hengzhou","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0685-0245","authenticated-orcid":false,"given":"Jincheng","family":"Tang","sequence":"additional","affiliation":[{"name":"Technology Innovation Center for Natural Resources Monitoring and Evaluation of Beibu Gulf Economic Zone, Ministry of Natural Resources, Nanning 530219, China"},{"name":"Guangxi Institute of Natural Resources Survey and Monitoring, Nanning 530201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1016\/j.scib.2022.07.015","article-title":"Measuring and evaluating SDG indicators with Big Earth Data","volume":"67","author":"Guo","year":"2022","journal-title":"Sci. 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