Abstract
Deep learning methods have made significant progress in the field of hyperspectral image (HSI) classification. However, these methods often rely on a large number of labeled samples, parameters, and computational resources to achieve state-of-the-art performance, which limits their applicability. To address these issues, this paper proposes a lightweight multiview mask contrastive network (LMCN) for HSI classification under small-sample conditions. Considering the influence of irrelevant bands, we construct two views in an HSI scene using band selection and principal component analysis (PCA). To enhance instance discriminability, we propose a combination of self-supervised mask learning and contrastive learning in the design of LMCN. Specifically, we train corresponding masked autoencoders using the obtained views and utilize the feature extraction part of the autoencoder as an augmentation function, conducting unsupervised training through contrastive learning. To reduce the number of parameters, we employ lightweight Transformer modules to construct the autoencoder. Experimental results demonstrate the superiority of this approach over several advanced supervised learning methods and few-shot learning methods under small-sample conditions. Furthermore, this method exhibits lower computational costs. Our code is available at https://github.com/Winkness/LMCN.git.
This work was supported in part by the Key Research and Development Project of Shanxi Province (No. 2021SF-429).
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Zhu, M., Wang, H., Meng, Y., Shan, Z., Ma, Z. (2024). Lightweight Multiview Mask Contrastive Network for Small-Sample Hyperspectral Image Classification. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_39
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DOI: https://doi.org/10.1007/978-981-99-8462-6_39
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