Sub-Band Grouping Spectral Feature-Attention Block For Hyperspectral Image Classification

Hyperspectral images (HSIs) consists of 2D spatial information and 1D spectral signature due to its speciality. Most models take the raw spectral signature as the input directly by regarding the spectral data as a sequence, which cannot fully explore the redundant and complementary information inside the spectral bands. In this paper, we proposed a novel sub-band grouping recurrent neural network (RNN) model with gated recurrent units (GRUs) to find the intrinsic feature in spectral information. We introduced the inter-band spectral cross-correlation measurement to see the high correlated groups of adjacent bands firstly. And then we concatenated the representative features from all groups for complementarity. The novel spectral feature-attention block was proposed to compound the mentioned steps and generated a much sparser feature representation for subsequent analysis. The experiment results illustrated the outstanding performances and got almost 1$\%$ and 5$\%$ improvement compared with the latest methods on two famous datasets.