Land cover classification of remote sensing images based on improved DeeplabV3+ network

Abstract

For land survey and land planning, the precise segmentation of land cover based on remote sensing images is extremely important. Based on the high-resolution satellite remote sensing images and the corresponding land cover GIS vector data in Hangzhou, for the categorization and change detection of urban land cover, a unique convolutional neural network is suggested. The data source is from high-resolution satellite remote sensing images, the semantic segmentation model DeeplabV3+ is improved. The original backbone network Xception is replaced by MobileNetV2 with smaller parameters, and add the channel attention module after the multi-scale module. In addition, to deal with the effect of category imbalance on model results, the focus loss function is introduced for network training. The pixel accuracy and average intersection ratio of 77.63% and 67.46% respectively, which are better than the commonly used semantic segmentation models DeeplabV3 +, UNet, and PSPNet models, and the model size is only 20.1MB. The experimental results show that the model is effective in redecing the number of model paramenters and improving the segmentation effect.