南京林业大学学报（自然科学版）2024，Vol.48Issue(1)：187-195,9.DOI:10.12302/j.issn.1000-2006.202204069
基于全卷积神经网络和低分辨率标签的森林变化检测研究
Research on forest change detection based on fully convolutional network and low resolution label
摘要
Abstract
[Objective]A forest change detection method based on fully convolutional networks and low resolution labels is proposed to address the problem of missing or insufficient high-precision label samples in current forest change detection,with the goal of achieving simple and rapid extraction of forest changes in forest areas.[Method]First,the gathered data was de-clouded,screened and labeled,and then the fully convolutional network model was used to extract the forests in high-scoring remote sensing photos in the study area in 2020 and 2021,respectively,and the model accuracy was evaluated.The forest change area was calculated using the post-classification comparison method,and the findings were compared with visual interpretation results.The pixel area was used to calculate evaluation indicators,such as forest change detection accuracy.[Result]Experiments reveal that the F1 score of the model employed in this research is 97.09%in 2020 forest extraction results and 95.96%in 2021 forest extraction results,which was the best among segmentation network models(U-Net,FPN,LinkNet).The total change precision rate of forest increase and forest decline was 73.30%,the recall rate was 77.37%,and the F1 score was 75.28%when comparing the forest extraction data from the two periods to obtain the changed area.[Conclusion]Based on low resolution labeling,this method allows for the speedy and precise capture of forest change regions from high-resolution remote sensing pictures.To accomplish forest change detection,a small number of low-resolution labels are used,which can also serve as a reference for large-scale forestland change inquiries.关键词
低分辨率标签/全卷积神经网络/深度学习/森林变化检测Key words
low resolution label/fully convolutional network/deep learning/forest changes detection分类
信息技术与安全科学引用本文复制引用
向俊,严恩萍,姜镓伟,宋亚斌,韦维,莫登奎..基于全卷积神经网络和低分辨率标签的森林变化检测研究[J].南京林业大学学报（自然科学版）,2024,48(1):187-195,9.基金项目
国家自然科学基金项目(32071682,31901311) （32071682,31901311）
国家林业和草原局中南调查规划设计院项目(68218022) （68218022）
湖南省林业科技创新计划(XLK202108-8). （XLK202108-8）
