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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References
Wambugu, N., et al.: Hyperspectral image classification on insufficient-sample and feature learning using deep neural networks: a review. Int. J. Appl. Earth Obs. Geoinf. 105, 102603 (2021)
Moharram, M.A., Sundaram, D.M.: Land use and land cover classification with hyperspectral data: a comprehensive review of methods, challenges and future directions. Neurocomputing (2023)
Su, Y., Li, X., Yao, J., Dong, C., Wang, Y.: A spectral-spatial feature rotation based ensemble method for imbalanced hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 61, 1–18 (2023)
Mou, L., Ghamisi, P., Zhu, X.X.: Deep recurrent neural networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 55(7), 3639–3655 (2017)
Zhu, M., Jiao, L., Liu, F., Yang, S., Wang, J.: Residual spectral-spatial attention network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(1), 449–462 (2020)
Guan, P., Lam, E.: Cross-domain contrastive learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 60, 1–13 (2022)
Liu, B., Yu, A., Yu, X., Wang, R., Gao, K., Guo, W.: Deep multiview learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(9), 7758–7772 (2020)
Hou, S., Shi, H., Cao, X., Zhang, X., Jiao, L.: Hyperspectral imagery classification based on contrastive learning. IEEE Trans. Geosci. Remote Sens. 60, 1–13 (2022)
Wang, M., Gao, F., Dong, J., Li, H., Du, Q.: Nearest neighbor-based contrastive learning for hyperspectral and lidar data classification. IEEE Trans. Geosci. Remote Sens. 61, 1–16 (2023)
He, K., Chen, X., Xie, S., Li, Y., Doll’ar, P., Girshick, R.B.: Masked autoencoders are scalable vision learners. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15979–15988 (2021)
Cai, Y., Zhang, Z., Liu, X., Cai, Z.: Efficient graph convolutional self-representation for band selection of hyperspectral image. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 13, 4869–4880 (2020)
Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42(8), 1778–1790 (2004)
Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework. IEEE Trans. Geosci. Remote Sens. 56(2), 847–858 (2017)
Liu, B., Yu, X., Yu, A., Zhang, P., Wan, G., Wang, R.: Deep few-shot learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 57(4), 2290–2304 (2018)
Li, Z., Liu, M., Chen, Y., Xu, Y., Li, W., Du, Q.: Deep cross-domain few-shot learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 60, 1–18 (2022). https://doi.org/10.1109/TGRS.2021.3057066
Wang, W., Liu, F., Liu, J., Xiao, L.: Cross-domain few-shot hyperspectral image classification with class-wise attention. IEEE Trans. Geosci. Remote Sens. 61, 1–18 (2023). https://doi.org/10.1109/TGRS.2023.3239411
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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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