Heterogeneous Spectral-Spatial Network With 3D Attention and MLP for Hyperspectral Image Classification Using Limited Training Samples

Methods based on convolutional neural networks (CNNs) have become a vital offshoot for hyperspectral image (HSI) classification. In recent years, the 3-D CNN (3-DCNN) has become dominant due to its excellent capability of extracting features. However, the high dimension and the limited training samples of HSI usually restrict the improvement of its classification accuracy. And the parameters of conventional 3-DCNN are larger so that computational complexity and running time increase. Therefore, a new model named HSSAM is proposed to solve the above problems. First, a 3-D residual-dense asymmetric convolution (3-D-RDAC) is designed to reuse the features, while reducing the parameters. Subsequently, 3-D-RDAC combined with the multiscale convolution to construct a 3-D multiscale RDAC (3-D-MRDAC) for avoiding the blind spots and unrecognized regions of receiving fields. Then, 3-D attention SimAM is applied to 3-D-MRDAC, for constituting the heterogeneous spectral-spatial attention convolutional neural (HSSAN) block, to extract spectral-spatial features of HSI adequately. Ultimately, MLP acts as the output layer of the model to better deal with the nonlinear features of HSI. Experiments in this article are carried out on four famous hyperspectral datasets: Indian Pines; Pavia University; WHU-Hi-LongKou; and WHU-Hi-HanChuan. Results show that HSSAM achieves better classification accuracy with limited training samples than several existing models. Overall accuracy reaches 96.84%, 98.85%, 98.01%, and 97.18% on the four datasets, respectively.