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To resolve above challenges, a semantic segmentation network for RS images is proposed, which employs an encoder\u2013decoder structure, where four ResNet-18 blocks act as encoders, and four specially designed local-contextual Transformer blocks form the decoder. In order to effectively utilize local contextual features, a channel attention-feature fusion module using a novel nonlinear spiking neuron model is designed to assist the decoder in better feature recovery. The experimental results demonstrate that the proposed method is feasible and effective for semantic segmentation of RS images. Specifically, the suboptimal 86.42% and optimal 82.25% mIoU are achieved on Potsdam and Vaihingen datasets, respectively, and the best 52.4% and the near-optimal 65.3% mIoU on the LoveDA and UAVid datasets, respectively, for the proposed model.<\/jats:p>","DOI":"10.1142\/s0129065726500292","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T05:33:08Z","timestamp":1772775188000},"source":"Crossref","is-referenced-by-count":0,"title":["Local-Contextual Feature Fusion Network Based on Nonlinear Spiking Neural Model for Semantic Segmentation of Remote Sensing Images"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1366-2285","authenticated-orcid":false,"given":"Junhao","family":"Du","sequence":"first","affiliation":[{"name":"School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. 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