Abstract
Recently, deep learning has achieved considerable results in hyperspectral image (HSI) classification. However, when training image classification models, existing deep networks require sufficient samples, which is expensive and inefficient in practical tasks. In this article, a novel Combining Spatial-spectral Features for Hyperspectral Image Few-shot Classification (CSFF) framework is proposed, attempting to accomplish the fine-grained classification with only a few labeled samples and train it with meta-learning ideas. Specifically, firstly, the spatial attention (SPA) and spectral query (SPQ) modules are introduced to overcome the constraint of the convolution kernel and consider the information between long-distance location (non-local) samples to reduce the uncertainty of classes. Secondly, the framework is trained by episodes to learn a metric space, and the task-based few-shot learning (FSL) strategy allows the model to continuously enhance the learning capability. In addition, the designed network not only discovers transferable knowledge in the source domain (SD) but also extracts the discriminative embedding features of the target domain (TD) classes. The proposed method can obtain satisfactory results with a small number of labeled samples. Extensive experimental results on public datasets demonstrate the versatility of CSFF over other state-of-the-art methods.
This work was supported by the National Natural Science Foundation of China under Grant 62161160336 and Grant 42030111.
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References
Chen, Y., Liu, Z., Xu, H., Darrell, T., Wang, X.: Meta-baseline: exploring simple meta-learning for few-shot learning. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9042–9051 (2021). https://doi.org/10.1109/ICCV48922.2021.00893
Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016). https://doi.org/10.1109/TGRS.2016.2584107
Gao, K., Liu, B., Yu, X., Qin, J., Zhang, P., Tan, X.: Deep relation network for hyperspectral image few-shot classification. Remote Sens. 12(6) (2020). https://doi.org/10.3390/rs12060923
He, J., Zhao, L., Yang, H., Zhang, M., Li, W.: Hsi-bert: hyperspectral image classification using the bidirectional encoder representation from transformers. IEEE Trans. Geosci. Remote Sens. 58(1), 165–178 (2020). https://doi.org/10.1109/TGRS.2019.2934760
Hong, D., Gao, L., Yao, J., Zhang, B., Plaza, A., Chanussot, J.: Graph convolutional networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 59(7), 5966–5978 (2021). https://doi.org/10.1109/TGRS.2020.3015157
Hong, D., et al.: More diverse means better: multimodal deep learning meets remote-sensing imagery classification. IEEE Trans. Geosci. Remote Sens. 59(5), 4340–4354 (2021). https://doi.org/10.1109/TGRS.2020.3016820
Hong, D., et al.: Spectralformer: rethinking hyperspectral image classification with transformers. IEEE Trans. Geosci. Remote Sens. 60, 1–15 (2022). https://doi.org/10.1109/TGRS.2021.3130716
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., Benediktsson, J.A.: Deep learning for hyperspectral image classification: an overview. IEEE Trans. Geosci. Remote Sens. 57(9), 6690–6709 (2019). https://doi.org/10.1109/TGRS.2019.2907932
Li, Y., Zhang, H., Shen, Q.: Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sens. 9(1) (2017). https://doi.org/10.3390/rs9010067
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
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 (2019). https://doi.org/10.1109/TGRS.2018.2872830
Long, M., Cao, Z., Wang, J., Jordan, M.I.: Domain adaptation with randomized multilinear adversarial networks. arXiv preprint arXiv:1705.10667 (2017)
Melgani, F., Bruzzone, L.: Support vector machines for classification of hyperspectral remote-sensing images. In: IEEE International Geoscience and Remote Sensing Symposium, vol. 1, pp. 506–508 (2002). https://doi.org/10.1109/IGARSS.2002.1025088
Sun, H., Zheng, X., Lu, X., Wu, S.: Spectral-spatial attention network for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 58(5), 3232–3245 (2020). https://doi.org/10.1109/TGRS.2019.2951160
Tuia, D., Persello, C., Bruzzone, L.: Domain adaptation for the classification of remote sensing data: an overview of recent advances. IEEE Geosci. Remote Sens. Magaz. 4(2), 41–57 (2016). https://doi.org/10.1109/MGRS.2016.2548504
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Zhou, Y., Ran, Q., Ni, L. (2022). Combining Spatial-Spectral Features for Hyperspectral Image Few-Shot Classification. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_35
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