Attention Network With Fractional-Ordered Mixed Geometric Features for Hyperspectral Image Classification

In recent years, the utilization of spectral and spatial information from adjacent pixels has become one of the key focuses in hyperspectral image (HSI) classification research, and the advent of the attention mechanism has introduced innovative approaches to this classification. However, existing applications of the attention mechanism primarily concentrate on the central pixel and its similar counterparts, overlooking cases when the central pixel is a noisy outlier. To remedy this defect, we introduce an attention network with fractional-ordered mixed geometric (ANFMG) features. First, we design differential geometric attention to differentiate between noise, edge, and flat areas. When the central pixel is a noisy outlier, differential geometric attention helps the model minimize its reliance on its own spectral information and reduces the effect of surrounding noise. Second, to achieve optimal classification outcomes with limited training samples, we introduce the specific fractal detail extraction strategy for targeted extraction of detailed information and minimizing redundant input. Furthermore, to focus on the central pixel and its neighboring pixels while mitigating the influence of pixels that exhibit spectral variability, we devise a fractal and similarity measurement fusion (FSMF) module, allocating limited resources to crucial features. The efficacy of our approach is confirmed through experiments on five HSI datasets, with results indicating superior classification performance.