2023/04/23 by Di Wang, Boxue Du, Wang, Di +5
Computer Science · Engineering · Mathematics · #Advanced Image and Video Retrieval Techniques #Artificial intelligence #Artificial neural network #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Convolution (computer science) #Data mining #Decomposition #FOS: Computer and information sciences #Hyperspectral imaging #Image (mathematics) #Image Retrieval and Classification Techniques #Kernel (algebra) #Mathematics #Pattern recognition (psychology) #Pixel #Remote-Sensing Image Classification
paper · pdf · doi:10.48550/arxiv.2304.11701
openalex publication_date 2023/04/23 · openalex created_date 2023/04/27 · openalex updated_date 2026/07/28
Recent neural architecture search (NAS) based approaches have made great progress in hyperspectral image (HSI) classification tasks. However, the architectures are usually optimized independently of the network weights, increasing searching time and restricting model performances. To tackle these issues, in this paper, different from previous methods that extra define structural parameters, we propose to directly generate structural parameters by utilizing the specifically designed hyper kernels, ingeniously converting the original complex dual optimization problem into easily implemented one-tier optimizations, and greatly shrinking searching costs. Then, we develop a hierarchical multi-module search space whose candidate operations only contain convolutions, and these operations can be integrated into unified kernels. Using the above searching strategy and searching space, we obtain three kinds of networks to separately conduct pixel-level or image-level classifications with 1-D or 3-D convolutions. In addition, by combining the proposed hyper kernel searching scheme with the 3-D convolution decomposition mechanism, we obtain diverse architectures to simulate 3-D convolutions, greatly improving network flexibilities. A series of quantitative and qualitative experiments on six public datasets demonstrate that the proposed methods achieve state-of-the-art results compared with other advanced NAS-based HSI classification approaches.