High-Resolution Remote Sensing Image Forest Land Change Detection Based on Deep Learning
Deep learning offers an efficient approach to forest land change detection by automatically extracting change features from dual-time-phase high-resolution images through large-scale data training,reducing reliance on operator experience and enhancing detection efficiency. Focusing on Zhejiang Province,this study utilized high-resolution remote sensing images from World Imagery Wayback and applied deep learning models,includ-ing UNet series,DeepLabV3 series,FCN,and SegNet,for binary classification of forest land changes. All models demonstrated strong performance on the test set,with mIoU above 84.70％,Accuracy above 92％,F1-score above 91.00％,and Recall above 91％. Among them,the UNet model achieved the best results,with mIoU of 89.54％,Accuracy of 95.15％,F1-score of 94.44％,and Recall of 94.13％,though all models showed varying degrees of edge contour blurring and incomplete intra-class connectivity. To address these limitations,the UNet model was improved using attention mechanisms,including SE (Squeeze-and-Excitation),SGAM (Spatial Gated Attention Mechanism),and CBAM (Convolutional Block Attention Module),and simi-lar enhancements were applied to DeepLabV3 and DeepLabV3P models for control experiments. Results showed that the UNet+SE model,leveraging channel attention,achieved the greatest improvement in mIoU (0.18％),while models enhanced with spatial attention (UNet+SGAM,DeepLabV3+SGAM,DeepLabV3P+SGAM) experienced decreases in mIoU by 1.01％,0.77％,and 0.67％,respectively,indicating that channel fea-tures are more critical than spatial features for forest land change detection. These findings confirm the UNet model's superior performance and high-light the importance of prioritizing channel features,providing valuable insights for forest land change detection using deep learning and high-resolu-tion remote sensing imagery.
万方数据
维普
