From Global to Local: A Dual-Branch Structural Feature Extraction Method for Hyperspectral Image Classification

Extracting discriminative spectral-spatial features from hyperspectral images (HSIs) remains a crucial topic within the remote sensing community. However, most feature extraction methods suffer from coarse textures, leading to poor performance in classifying HSIs. Moreover, limited training samples can significantly hinder the generalization ability of classifiers, hindering performance improvements in HSI classification. To address these issues, a proposed dual-branch structural feature extraction method fully leverages the global and local information, preserving key structures while removing unimportant textures. The key steps of our method are detailed below: First, an image fusion technique is performed on HSIs to obtain dimension-reduced HSIs. Second, a global multiscale feature extraction method is proposed by performing different parameter settings of pyramid texture filtering (PTF) on HSIs and global probability can be obtained. Third, a PTF-based local multiscale feature extraction method is proposed using multiscale superpixel segmentation on the finest layer of PTFs and local probability can be obtained. Fourth, a decision fusion rule is applied to integrate class probabilities from the two branches. Experiments performed on three different datasets demonstrate that the proposed method can outperform other state-of-the-art classification methods.