Fusing Multi-scale Features for Segmentation of High Resolution Forest Remote Sensing Images

To achieve the effective monitoring of plateau forests in the Sanjiangyuan National Nature Reserve in Qinghai, a fusing multi-scale features remote sensing image segmentation algorithm based on deep learning technology was proposed. First, the first 2 m spatial resolution plateau forest dataset in the region was constructed. Second, to solve the problem of insufficient ground-truth label of remote sensing images which affects the training of network models, a data augmentation method of shuffling and reorganizing images was proposed according to the characteristics of forest remote sensing image segmentation, and the training data was expanded to 1 600 images. To address the problem of mainstream segmentation networks that cannot focus on details in processing large-scale remote sensing images, a fusing multi-scale features high-resolution forest remote sensing image segmentation network model based on encoding and decoding structures was proposed. The model incorporated the designed convolution block, multi-scale feature fusion block and feature amplification extraction block. Results show that the data augmentation algorithm proposed improves the segmentation accuracy of the model, while the proposed model trained by the proposed data augmentation achieves an intersection over union (IoU) of 89.64%, and the results are better than that of the current mainstream image segmentation models.