Forest Change Detection based on Siamese Neural Network with GF-2 Image:A Case of Jiande Forest Farm,Zhejiang
Forest is a valuable non-renewable resource,but the ecological environment of forest is seriously threatened by many natural or man-made factors such as fire,flood,and deforestation interference.Accurate grasp of forest resource changes can provide effective information for forest resource management and protec-tion.In the task of forest change detection,traditional machine learning change detection methods have difficul-ty in capturing deep semantic information due to large differences in forest categories and tree species,and suffer from poor adaptability of extracted features,weak recognition ability,and pseudo-change due to seasonal phas-es.We propose to build a deep learning model with Siamese neural networks for forest change detection experi-ments.Three different feature extraction methods,ResNet50(Residual neural network),CBAM(Convolu-tional Block Attention Module)and SE(Squeeze and Excitation)with different lightweight attention mecha-nisms are used as backbone feature extraction modules,respectively.All three backbone feature extraction net-works are trained based on pre-trained weights,which improve change detection by fusing the extracted multi-scale feature maps so that the coarse and fine details of information in different feature maps complement each other.It also has the advantage of sharing weights with the same number of parameters.Taking Jiande Forest Farm in Zhejiang province as the experimental area,two phases of GF-2 images in 2015 and 2020 are acquired to construct a forest change detection dataset with a resolution of 1m.The results of Siamese neural network change detection are compared with the true change labels(Ground truth),where the backbone feature extrac-tion network SE-ResNet50 has the best combined results with Precision(0.91),Recall(0.78)and F1-score(0.83),which is better than mainstream change detection models FC-Siam-conc,FC-Siam-diff.It is proved that Siamese neural networks can accurately capture forest changes in the task of forest lad change detection from high-resolution remote sensing images,and provide a new forest change detection method for forest re-source management departments.
NETL
NSTL
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