High-resolution satellite image segmentation method based on improved DeepLabV3+
As to the problems of poor edge segmentation accuracy and feature information loss when DeepLabV3+is used to perform high-resolution satellite image segmentation,this paper proposes a high-resolution satellite image segmentation method based on improved DeepLabV3+.Firstly,the expansion rate of the atrous spatial pyramid pooling(ASPP)module is adjusted to better adapt the model to the features of high-resolution images and improve the feature extraction capability.Then,the convolutional block attention module(CBAM)is introduced in the encoding stage to dynamically optimize the weight and location information of channels and enhance the learning of key feature information of images.Finally,the Adaptively Spatial Feature Fusion(ASFF)module is introduced in the decoding stage to aggregate features of different scales into the input features of the decoder and im-prove the accuracy of the semantic segmentation.The results show that the overall accuracy,precision and intersection ratio from the proposed method are significantly improved compared with those from the traditional semantic segmentation,it can be used to effectively solve the problem of high-resolution satellite image segmentation.
万方数据
维普
