Automatic Clustering-Based Two-Branch CNN for Hyperspectral Image Classification

It is observed that the great spectral variation in the same hyperspectral image (HSI) pixel class often leads to misclassification. To solve this problem, we have proposed an automatic clustering-based two-branch convolutional neural network (CNN): first, to reduce the intraclass spectral variation, the HSI pixels are automatically subdivided into smaller classes by clustering; second, in order to suppress the interference of spectral amplitude variation, the SincNet is introduced to capture the spectral pattern by giving more weight to the spectral shape; third, the DS-CNN with double directional strip convolution kernel is designed to extract spatial feature, so that specific contextual interactional features can be collected, especially in strip-shaped field-like roads and farmlands; finally, the spectral and spatial features extracted by the two branches are fused at fully connected layer to obtain an accurate classification. Extensive experiments demonstrated that the proposed method can obtain better classification performance than the state-of-the-art methods.