Remote sensing image feature segmentation method based on deep learning

In view of the difficulty of traditional methods for simultaneous and effective multi-target segmentation, the existing ground object classification methods based on fully convolutional neural networks have low classification accuracy in complex scenes, this paper proposes an improved encoder-decoder based on the U-shaped network structure DL-Unet, which realizes the effective segmentation of remote sensing images. This network improves the traditional convolution method and introduces the expanded convolution, which increases the receptive field without increasing the network parameters. Aiming at the problem of imbalance in the clssification of featares in remote sensing images, weighted cross-entropy is used as the loss function of the model, which effectively overcomes the selection preference of the model function of the model effectively. The relative majority voting strategy is adopted for the prediction results to further improve the pixel accuracy (PA) of each feature category. The experimental results show that the mean pixel accuracy (MPA) and mean intersection over union (MIoU) of this model are improved by 5.94% and 9.45% respectively compared with the classic U-net, which verifies that the method in this paper is an effective remote sensing image classification method.