Lightweight Multiscale Neural Architecture Search With Spectral–Spatial Attention for Hyperspectral Image Classification

Hyperspectral image (HSI) classification based on neural architecture search (NAS) is a currently attractive frontier as it not only automatically searches complex neural network architecture, but also avoids professional knowledge and experience design, and alleviates the lacking of generalization ability as well when dealing with a new classification task. However, the existing HSI classification based on NAS has some drawbacks: 1) A huge number of training parameters and high calculations are inductive to over-fitting and high complexity. 2) Efficient operators are lacking in the search space which can distinguish spatial locations and spectral features in different bands. Furthermore, as the category samples in HSI data show a serious long-tail distribution phenomenon, HSI classification remains challenging. To address these issues, we propose a lightweight HSI classification model LMSS-NAS integrating multi-scale spectral-spatial attention. The main work includes three-fold: 1) In order to reduce the number of model parameters and promote spectral-spatial feature fusion, a new lightweight efficient search space is designed, which consists of three equivalent lightweight convolution operators with multiple receptive fields. 2) To fully use the spectral-spatial correlation of HSI, a cube-to-pixel classification framework is designed to mine the local spatial and spectral context. 3) Focal loss and label smoothing loss in computer vision tasks are jointly migrated to LMSS-NAS to improve the unbalanced samples' classification and model robustness. Experimental results on four public hyperspectral data sets show that the proposed method can achieve competitive classification performance as well as low computational cost. Code is available at: https://github.com/xh-captain/LMSS-NAS.