Combine Superpixel-Wise GCN and Pixel-Wise CNN for Polsar Image Classification

Superpixel-based graph convolution network (SGCN) can extract global features well and reduce computing time greatly, which has been widely used in image classification. However, SGCN ignores individual feature for each pixel within a superpixel. Pixel-wise convolutional neural network (CNN) can learn local features with fixed-square convolution kernel. Combine with both the advantages of SGCN and CNN, we proposed a novel SGCN-CNN method, which can combine the global and local features together. Superpixel-wise SGC-N and pixel-wise CNN cannot be combined into a network directly since they are with different scales. To alleviate this issue, encoder and decoder are designed by defining an association matrix, which can covert features between superpixel and pixel. In addition, complex matrix-based Wishart metric is used to construct the edge weights for SGCN. The proposed method can obtain both global and local features to improve classification performance. Experimental results demonstrate the effectiveness of the proposed method.