Concluding Remarks and Way Forward
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-42667-4_8
Reference16 articles.
1. Pande, S. & Banerjee, B. (2021). Attention based convolution autoencoder for dimensionality reduction in hyperspectral images. In 2021 IEEE international geoscience and remote sensing symposium IGARSS, Brussels, Belgium, pp. 2727–2730. https://doi.org/10.1109/IGARSS47720.2021.9553019
2. Paul, A., Bhattacharya, S., Dutta, D., Sharma, J.R., & Dadhwal, V. K. (2015). Band selection in hyperspectral imagery using spatial cluster mean and genetic algorithms. GIScience and Remote Sensing, 52(6), 644–661. https://doi.org/10.1080/15481603.2015.1075180
3. Paul, A., & Chaki, N. (2019). Dimensionality Reduction of Hyperspectral Images Using Pooling. Pattern Recognit. Image Anal. 29, 72–78. https://doi.org/10.1134/S1054661819010085
4. Paul, A., Sahoo, P., & Chaki, N. (2020). Dimensionality Reduction of Hyperspectral Images: A Data-Driven Approach for Band Selection. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 1136. Springer, Singapore. https://doi.org/10.1007/978-981-15-2930-6_2
5. Paul, A., & Bhoumik, S. (2022). Classification of hyperspectral imagery using spectrally partitioned HyperUnet. Neural Computing and Applications, 34, 2073–2082. https://doi.org/10.1007/s00521-021-06532-3
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