Genetic Algorithm Framework for 3D Discrete Wavelet Transform based Hyperspectral Image Classification
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12524-024-01850-0.pdf
Reference23 articles.
1. Anand, R., Veni, S., & Aravinth, J. (2021). Robust classification technique for hyperspectral images based on 3D discrete wavelet transform. Remote Sensing, 13, 1255. https://doi.org/10.3390/rs13071255
2. Burrus, C. S., Gopinath, R. A., & Guo, H. (1998). Introduction to wavelets and wavelet transforms: A primer. New Jersey: Prentice Hall.
3. Cao, X., Yao, J., Fu, X., & Bi, H. (2020). An enhanced 3D discrete wavelet transform for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 18(6), 1104–1108. https://doi.org/10.1109/LGRS.2020.2990407
4. Chen, Y., Zhao, X., & Lin, Z. (2014). Optimizing subspace SVM ensemble for hyperspectral imagery classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 1295–1305. https://doi.org/10.1109/JSTARS.2014.2307356
5. Fatourechi, M., Birch, G. E., & Ward, R. K. (2007). Application of a hybrid wavelet feature selection method in the design of a self-paced brain interface system. Journal of Neuro Engineering and Rehabilitation, 4, 1–13. https://doi.org/10.1186/1743-0003-4-11
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