Multi-Scale Fuzzy Graph Convolutional Network for Hyperspectral Image Classification

Hyperspectral image classification methods based on graph convolution network have received extensive attention. However, the traditional distance metric is difficult to fully represent the spectral variability and uncertainty in hyperspectral images. In order to alleviate this problem, a multi-scale fuzzy graph convolutional network is constructed for hyperspectral image classification. In detail, the SLIC algorithm is used to perform superpixel segmentation of hyperspectral images. Each superpixel is regarded as a graph node, and a fuzzy measurement mechanism is introduced to measure the similarity between two nodes to describe the uncertainty between pixels in the hyperspectral image, so as to construct a fuzzy graph convolution. Subsequently, the fuzzy graph convolution is extended to multi-scale to capture the rich contextual information within the hyperspectral image. In the training process, the pixel-level features are integrated into the superpixel-level graph update process to establish the connection between the pixel level and the superpixel level. Finally, experimental results on two publicly available hyperspectral image datasets show that the proposed network outperforms other representative peers.