Enhanced High-Frequency Spatial Feature Networks for Hyperspectral Images Classification

The efficient extraction of spatial-spectral features is crucial for hyperspectral images (HSIs) classification, especially the spatial features are essential to improve the classification accuracy. Both the convolutional neural network (CNN) and transformers frameworks can effectively represent high-level global and local semantic features, which have significant potential for HSIs classification field. We proposed an enhanced high-frequency spatial feature (EH-FS2) method for HSIs classification. First, we connected two networks, CNN and transformer, as a shallow-deep feature extractor to rapidly extract multiscale spectral-spatial features by CAB attention module. This module not only realizes the efficient association of local and global features but also emphasizes the important spatial location information. Then, we added a local deep convolutional (L-D-ConvFFN) to the GLS2FFN module to effectively compensate for the loss of high-frequency spatial information by the self-attention mechanism as a low-pass filter and enhance the capability of perceiving high-frequency spatial details. The experiments were validated on the Salinas (SAs), Pavia University (PU), and WHU-Hi-LongKou datasets, and the classification performance outperforms several state-of-the-art approaches. The experiments have shown that the EH-FS2 method can better distinguish the features between different classes, especially for the enhancement and recovery of high-frequency spatial features with significant advantages. Furthermore, it can also be proved that the generalization of the HSI classification problem to the advanced semantic classification problem is validated.