北京测绘2025，Vol.39Issue(11)：1615-1620,6.DOI:10.19580/j.cnki.1007-3000.2025.11.009
基于改进U-Net模型的海岸带典型地物遥感自动监测
Remote sensing automatic monitoring of typical coastal features based on an improved U-Net method
摘要
Abstract
This paper addressed the challenges of complex feature distribution and small target classification in the coastal wetlands of the Yellow River Estuary.It proposed a remote sensing automatic monitoring of typical coastal features based on an improved convolutional networks for biomedical image segmentation(U-Net)model.Building upon the classic U-Net framework,convolutional layers were stacked to expand the receptive field,and a convolutional block attention module(CBAM)was introduced to improve the extraction of spectral and spatial features.The study area was located at the estuary of the Yellow River in Shandong Province,and Sentinel-2 satellite imagery from 2023 was used.Data preprocessing and augmentation were performed to generate a sample set for model training and evaluation.Experimental results show that the improved model achieves overall classification accuracy(OA)of 92.73%,and the mean intersection over union(MIoU)increases to 77.68%,representing improvements of 2%and 4.3%,respectively,compared to the traditional U-Net model.The study addresses the issue of misclassification of spectrally similar features and significantly enhances the classification performance for small targets(such as Suaeda salsa)and linear features(such as tidal channels).It ensures the completeness and connectivity of feature classification.This paper provides a new technical approach for remote sensing classification of complex coastal wetland ecosystems and offers scientific evidence for wetland protection and management.关键词
遥感分类/黄河口滨海湿地/深度学习/U型网络改进模型/卷积块注意力机制Key words
remote sensing classification/Yellow River estuary coastal wetlands/deep learning/improved convolutional networks for biomedical image segmentation(U-Net)model/convolutional block attention mechanism分类
测绘与仪器引用本文复制引用
姚斌,许豪刚,王溆栋,张晨航..基于改进U-Net模型的海岸带典型地物遥感自动监测[J].北京测绘,2025,39(11):1615-1620,6.基金项目
浙江省自然资源厅2022科研项目(2022-57) （2022-57）
