Remote Scene Image Scene Classification Based on Adaptive Segmentation and Dynamic Graph Convolution

As an important research topic in the remote sensing (RS) community, RS image scene classification is a challenging task due to the complex contents of RS images. In general, RS image scene classification is a single-label problem. Nevertheless, it is known that the contents within RS are huge in volume and diverse in type. Only a single semantic label cannot describe an RS scene completely, especially when the resolution of RS images is increased recently. The various semantics hidden in the high-resolution RS (HRRS) images are also important to the scene classification task. Taking the issues mentioned above into account, we develop a new scene classifier named graph scene classifier (GSCer) for HRRS images with the help of the deep convolution neural network (DCNN) and dynamic graph convolution (DGCN). Not only the global semantic but also the diverse hidden local semantics within an HRRS image can be fully explored. The encouraging experimental results counted on two public HRRS data sets demonstrate that our GSCer is effective in HRRS scene classification tasks.