A hyperspectral unmixing model using convolutional vision transformer

Author:

Bhakthan Sreejam Muraleedhara,Loganathan Agilandeeswari

Publisher

Springer Science and Business Media LLC

Reference41 articles.

1. Aberna P, Agilandeeswari L, Bansal A (2023) Vision Transformer-Based Watermark Generation for Authentication and Tamper Detection Using Schur Decomposition and Hybrid Transforms. International Journal of Computer Information Systems and Industrial Management Applications 15(2023):107–121

2. Aberna P, Agilandeeswari L (2024) Optimal semi-fragile watermarking based on maximum entropy random walk and swin transformer for tamper localization. IEEE Access 1–1. https://doi.org/10.1109/ACCESS.2024.3370411

3. Agilandeeswari L, Prabukumar M, Sathishkumar S (2017) An efficient hybrid approach for hyperspectral image classification. Int J Pure Appl Math 117(17):135–141

4. Agilandeeswari L, Prabukumar M, Radhesyam V, Phaneendra KLNB, Farhan A (2022) Crop classification for agricultural applications in hyperspectral remote sensing images. Appl Sci (Switzerland) 12(3). https://doi.org/10.3390/app12031670

5. Agilandeeswari L, Meena SD (2023) SWIN transformer-based contrastive self-supervised learning for animal detection and classification. Multimed Tools Appl 82(7):10445–10470. https://doi.org/10.1007/s11042-022-13629-x

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