3D-ANAS v2: Grafting Transformer Module on Automatically Designed ConvNet for Hyperspectral Image Classification
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Hyperspectral image (HSI) classification has been a hot topic for decides, as Hyperspectral image has rich spatial and spectral information, providing strong basis for distinguishing different land-cover objects. Benefiting from the development of deep learning technologies, deep learning based HSI classification methods have achieved promising performance. Recently, several neural architecture search (NAS) algorithms are proposed for HSI classification, which further improve the accuracy of HSI classification to a new level. In this paper, we revisit the search space designed in previous HSI classification NAS methods and propose a novel hybrid search space, where 3D convolution, 2D spatial convolution and 2D spectral convolution are employed. Compared search space proposed in previous works, the serach space proposed in this paper is more aligned with characteristic of HSI data that is HSIs have a relatively low spatial resolution and an extremely high spectral resolution. In addition, to further improve the classification accuracy, we attempt to graft the emerging transformer module on the automatically designed ConvNet to adding global information to local region focused features learned by ConvNet. We carry out comparison experiments on three public HSI datasets which have different spectral characteristics to evaluate the proposed method. Experimental results show that the proposed method achieves much better performance than comparison approaches, and both adopting the proposed hybrid search space and grafting transformer module improves classification accuracy. Especially on the most recently captured dataset Houston University, overall accuracy is improved by up to nearly 6 percentage points. Code will be available at: https://github.com/xmm/3D-ANAS-V2.
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