计算机工程与应用2024，Vol.60Issue(14)：357-366,10.DOI:10.3778/j.issn.1002-8331.2304-0355
结合HDC和Attention的高分遥感影像光伏板提取研究
Research on Extracting Photovoltaic Panels from High Resolution Remote Sensing Images by Combining HDC and Attention
摘要
Abstract
Manual investigation and maintenance of photovoltaic panels result in expensive human maintenance costs.Therefore,using deep learning methods to extract photovoltaic panels from remote sensing images can provide important data support for the operation and maintenance of photovoltaic power generation scenarios at a low cost.The improved DeepLabV3+semantic segmentation model has been used to solve the problem of precise segmentation and extraction of photovoltaics using high-resolution remote sensing images.It proposes a method for extracting photovoltaic panels from ultra-high resolution remote sensing images based on DeepLabV3+deep learning architecture.The main innovative work is reflected in:(1)a hybrid cavity convolutional spatial pyramid pooling module is proposed to address the problem of difficulty in accurately extracting photovoltaic panel information from remote sensing images;(2)to address the issue of edge details being easily lost in photovoltaic panel information extraction,attention mechanism is introduced to sensi-tively capture small domain features to improve the segmentation ability of the model.This research uses the multi-resolution photovoltaic data set published by the University of the Chinese Academy of Sciences in 2021 to conduct experiments.The results show that the proposed model can achieve 92.54％,79.91％and 76.27％of the distributed photovoltaic data set IoU with 0.1 m,0.3 m and 0.8 m spatial resolution.Compared to the original DeepLabV3+model,the ground photo-voltaic dataset with spatial resolutions of 0.3 m and 0.8 m can achieve 94.27％and 87.24％IoU improvement of 0.13～2.02 percentage points in three different resolutions and backgrounds.Compared with the classical semantic segmentation models U-Net,PSPNet,and DeepLabV3+,the proposed method improves IoU by 0.64～20.51 percentage points on roof-top distributed photovoltaic datasets with spatial resolutions of 0.1 m,0.3 m,and 0.8 m.The above experiments demon-strate the effectiveness of this method.关键词
高分辨率遥感/光伏板识别与提取/语义分割/混合空洞卷积/注意力机制Key words
high-resolution remote sensing/PV panel identification and extraction/semantic segmentation/mixed void convolution/attention mechanism分类
管理科学引用本文复制引用
刘桂生,丁鑫,祝锐,张天健,狄兮尧,薛朝辉..结合HDC和Attention的高分遥感影像光伏板提取研究[J].计算机工程与应用,2024,60(14):357-366,10.基金项目
国家自然科学基金(42271324). （42271324）
