Learning transferable cross-modality representations for few-shot hyperspectral and LiDAR collaborative classification

Author:

Dai MofanORCID,Xing ShuaiORCID,Xu Qing,Wang HanyunORCID,Li Pengcheng,Sun YifanORCID,Pan Jiechen,Li Yuqiong

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Computers in Earth Sciences,Earth-Surface Processes,Global and Planetary Change

Reference50 articles.

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2. Eitel, A., Springenberg, J.T., Spinello, L., Riedmiller, M.A., Burgard, W., 2015. Multimodal deep learning for robust RGB-D object recognition, in: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, Hamburg, Germany, September 28 - October 2, 2015. IEEE, pp. 681–687. 10.1109/IROS.2015.7353446.

3. S2ENet: Spatial-Spectral Cross-Modal Enhancement Network for Classification of Hyperspectral and LiDAR Data;Fang;IEEE Geosci. Remote Sensing Lett.,2022

4. Hyperspectral Image Classification Based on Semisupervised Self-Learning and Multiview Information Fusion;Feng;IEEE Geosci. Remote Sensing Lett.,2022

5. Finn, C., Abbeel, P., Levine, S., 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, in: Precup, D., Teh, Y.W. (Eds.), Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, Proceedings of Machine Learning Research. PMLR, pp. 1126–1135.

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