Hyperspectral Image Classification With Multi-Attention Transformer and Adaptive Superpixel Segmentation-Based Active Learning

Deep learning (DL) based methods represented by convolutional neural networks (CNNs) are widely used in hyperspectral image classification (HSIC). Some of these methods have strong ability to extract local information, but the extraction of long-range features is slightly inefficient, while others are just the opposite. For example, limited by the receptive fields, CNN is difficult to capture the contextual spectral-spatial features from a long-range spectral-spatial relationship. Besides, the success of DL-based methods is greatly attributed to numerous labeled samples, whose acquisition are time-consuming and cost-consuming. To resolve these problems, a hyperspectral classification framework based on…

• NN
  National Natural Science Foundation of China

  Awards: 62002083, 61971153, 62071136
• NS
  Natural Science Foundation of Heilongjiang Province

  Award: LH2021F012
• SK
  State Key Laboratory on Integrated Optoelectronics

  Award: OFSLRSS202210