  Hyperspectral images involve abundant spectral and spatial information,
playing an irreplaceable role in land-cover classification. Recently, based on
deep learning technologies, an increasing number of HSI classification
approaches have been proposed, which demonstrate promising performance.
However, previous studies suffer from two major drawbacks: 1) the architecture
of most deep learning models is manually designed, relies on specialized
knowledge, and is relatively tedious. Moreover, in HSI classifications,
datasets captured by different sensors have different physical properties.
Correspondingly, different models need to be designed for different datasets,
which further increases the workload of designing architectures; 2) the
mainstream framework is a patch-to-pixel framework. The overlap regions of
patches of adjacent pixels are calculated repeatedly, which increases
computational cost and time cost. Besides, the classification accuracy is
sensitive to the patch size, which is artificially set based on extensive
investigation experiments. To overcome the issues mentioned above, we firstly
propose a 3D asymmetric neural network search algorithm and leverage it to
automatically search for efficient architectures for HSI classifications. By
analysing the characteristics of HSIs, we specifically build a 3D asymmetric
decomposition search space, where spectral and spatial information are
processed with different decomposition convolutions. Furthermore, we propose a
new fast classification framework, i,e., pixel-to-pixel classification
framework, which has no repetitive operations and reduces the overall cost.
Experiments on three public HSI datasets captured by different sensors
demonstrate the networks designed by our 3D-ANAS achieve competitive
performance compared to several state-of-the-art methods, while having a much
faster inference speed.
