Pattern recognition (psychology)

Full spectral imaging

Hyperspectral imaging

Pixel

Artificial intelligence

Spatial analysis

Composite material

Exploit

Geography

Materials science

Range (aeronautics)

Subnetwork

Computer science

Computer vision

Computer security

Remote sensing

IEEE Transactions on Geoscience and Remote Sensing

This study presents a spectral–spatial self-attention network (SSSAN) for classification of hyperspectral images (HSIs), which can adaptively integrate local features with long-range dependencies related to the pixel to be classified. Specifically, it has two subnetworks. The spatial subnetwork introduces the proposed spatial self-attention module to exploit rich patch-based contextual information related to the center pixel. The spectral subnetwork introduces the proposed spectral self-attention module to exploit the long-range spectral correlation over local spectral features. The extracted spectral and spatial features are then adaptively fused for HSI classification. Experiments conducted on four HSI datasets demonstrate that the proposed network outperforms several state-of-the-art methods.

Spectral–Spatial Self-Attention Networks for Hyperspectral Image Classification