Residual Spectral–Spatial Attention Network for Hyperspectral Image Classification

In the last five years, deep learning has been introduced to tackle the hyperspectral image (HSI) classification and demonstrated good performance. In particular, the convolutional neural network (CNN)-based methods for HSI classification have made great progress. However, due to the high dimensionality of HSI and equal treatment of all bands, the performance of these methods is hampered by learning features from useless bands for classification. Moreover, for patchwise-based CNN models, equal treatment of spatial information from the pixel-centered neighborhood also hinders the performance of these methods. In this article, we propose an end-to-end residual spectral-spatial attention network (RSSAN) for HSI…

• NN
  National Natural Science Foundation of China

  Awards: 61871310, 61621005, U1701267, 91438201, 61573267, 61801353, 61836009, 91438103
• FR
  Fundamental Research Funds for the Central Universities

  Award: JB191907