  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.
