Image-based neural architecture automatic search method for hyperspectral image classification

Convolutional neural networks (CNNs) have shown excellent performance for hyperspectral image (HSI) classification due to their characteristics of both local connectivity and sharing weights. Nevertheless, with the in-depth study of network architecture, merely manual empirical design can no longer meet the current scenario needs. In addition, the existing CNN-based frameworks are heavily affected by the redundant three-dimensional cubes of the input and result in inefficient description issues of HSIs. We propose an image-based neural architecture automatic search framework (I-NAS) as an alternative to CNN. First, to alleviate the redundant spectral-spatial distribution, I-NAS feeds a full image into the framework via a label masking fashion. Second, an end-to-end cell-based structure search space is considered to enrich the feature representation. Then, it determined the optimal cells by employing a gradient descent search algorithm. Finally, the well-trained CNN architecture is automatically constructed by stacking the optimal cells. The experimental results from two real HSI datasets indicate that our proposal can provide a competitive performance in classification. (C) The Authors.