现代信息科技2024，Vol.8Issue(17)：43-48,55,7.DOI:10.19850/j.cnki.2096-4706.2024.17.009
基于改进PSPNet的森林火烧迹地检测
Forest Burned Area Detection Based on Improved PSPNet
摘要
Abstract
In order to improve the detection accuracy of forest burned area,this paper uses Sentinel-2 satellite images after the fire to propose a forest burned area detection model based on improved PSPNet.This model employs ResNet34 with dilated convolution as the backbone network and fuses the RFB module and ULSAM module inside the backbone network to enhance its feature extraction capability.Finally,skip connection is used to make the decoder part of the model make full use of the four-level feature maps output by the backbone network.The experimental results show that the MIoU and overall accuracy of the improved PSPNet model is 91.86%and 96.89%,respectively,which is 1.52%and 0.67%higher than PSPNet.Compared with other semantic segmentation models,segmentation outcomes achieved by the improved model exhibit richer details and have better generalization performance.关键词
森林火灾/火烧迹地/多光谱卫星影像/深度学习/语义分割Key words
forest fire/burned area/multispectral satellite image/Deep Learning/semantic segmentation分类
信息技术与安全科学引用本文复制引用
张艺,马永军,王广来,黄建平..基于改进PSPNet的森林火烧迹地检测[J].现代信息科技,2024,8(17):43-48,55,7.基金项目
中央高校基本科研业务费专项资助基金(2572019CP19) （2572019CP19）
