数据与计算发展前沿2026，Vol.8Issue(2)：25-39,15.DOI:10.11871/jfdc.issn.2096-742X.2026.02.003
基于自适应语义连接和感知注意力的沙漠分割方法
Desert Segmentation Based on Adaptive Semantic Connectivity and Perceptual Attention
摘要
Abstract
[Background]Desertification is recognized as a severe land degradation process threatening economic development and ecological security.Satellite remote sensing imagery is utilized for its wide coverage and high resolution,making deep neural networks and remote sensing tech-niques critical for desert boundary extraction in scientific research and sustainable develop-ment.[Methods]Inspired by this,a hybrid network model based on adaptive semantic connec-tivity is proposed,where global context and local texture features are fused to effectively re-duce semantic discrepancies between encoder and decoder,thereby enhancing desert boundary segmentation accuracy.To address high computational complexity and insufficient generalization capability,a dy-namic feature-scaled multi-head self-attention mechanism is designed,which is combined with a residual convo-lutional block attention module to strengthen multi-scale feature capture.Additionally,a differentiable boundary metric is introduced as a loss function to optimize boundary continuity.[Results]Experiments conducted on the Landsat-8 dataset demonstrate superior performance in both visual interpretation and quantitative evaluation met-rics,and a high-precision technical solution is provided for desertification monitoring.关键词
深度学习/语义分割/遥感/自适应语义连接Key words
deep learning/semantic segmentation/remote sensing/adaptive semantic connectivity引用本文复制引用
王兆滨,王睿,吕永科,张耀南..基于自适应语义连接和感知注意力的沙漠分割方法[J].数据与计算发展前沿,2026,8(2):25-39,15.基金项目
国家重点研发计划基础科研条件与重大科学仪器设备研发专项"冰冻圈大数据挖掘分析关键技术及应用"(2022YFF07117) （2022YFF07117）
