Dual-Scale Complementary Spatial-Spectral Joint Model for Hyperspectral Image Classification

In the field of hyperspectral image classification, using spatial information as a supplement to spectral information has been widely applied. This article proposes a novel dual-scale complementary spatial-spectral joint classification model (DSCSM) to mitigate the issues of detail loss and insufficient utilization of spatial information, which traditionally lead to lower classification accuracy. In essence, the final classification result is obtained through decision fusion of two complementary feature extraction stages. In the preprocessing stage, a new dual-scale truncated filtering feature extraction method (DTFE) is proposed, which uses truncated filters with two different parameter settings to obtain two scales of smoothed patches, and then fuses them to obtain dual-scale structural features using Kernel principal component analysis. DTFE preserves edge information while smoothing details, effectively removing noise and retaining the dual-scale feature information. In the postprocessing stage, a sub-Markov random walk-based spatial probability optimization method is proposed, which models the spatial association of neighboring pixels, retaining complex textures as well as weak edge information to optimize the classification probability. Finally, the decision fusion strategy is employed to integrate the classification probabilities acquired from the aforementioned two stages. Comparative experiments on six different scene datasets with state-of-the-art classification methods validate that even with a small number of samples, DSCSM can achieve excellent object recognition performance. In addition, comprehensive parameter analysis proves the robustness and stability of the proposed method.