Masked Topology Convolutional Network for Classification and Segmentation of Remote Sensing Images

Convolutional neural networks have made significant progress in remote sensing image processing. Convolutional networks mostly model the local information of samples based on pixel features, while ignoring the topological relationship among different categories of ground objects. However, the nonlocal topology can better represent the underlying data structure of the image. In order to make up for the insufficiency of Convolutional neural networks in extracting nonlocal topological information, we propose a new masked topology convolutional network (MTC_Net). First, considering the regionality of different categories of ground objects, we use a clustering algorithm to simply cluster the pixels to extract the most obvious area mask, which can strengthen the boundary information among different ground objects. Second, we take the cluster center point as the representative point of the region and use the graph structure to represent the topological relationship, in addition use graph convolution to further extract the topological structure relationship among different categories of ground objects. Finally, we use convolution modules and multilevel feature attention modules to capture important local and global contextual semantic information and reduce category confusion. We conduct a great deal of experiments on four (two classification and two segmentation) public datasets, and the proposed MTC_Net achieves excellent classification and segmentation performance.