Automated Classification of Hyper Spectral Image Using Supervised Machine Learning Approach
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-4831-2_63
Reference19 articles.
1. Lv W, Wang X (2020) Overview of hyperspectral image classification. J Sens 1–13. https://doi.org/10.1155/2020/4817234
2. Joy AA, Hasan MAM, Hossain MA (2019) A comparison of supervised and unsupervised dimension reduction methods for hyperspectral image classification. In: 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’sBazar, Bangladesh, pp 1–6. https://doi.org/10.1109/ECACE.2019.8679360
3. Younus L, Kasapoglu NG (2019) Dimension reduction and its effects in hyperspectral data classification. In: 2019 6th International Conference on Electrical and Electronics Engineering (ICEEE), Istanbul, Turkey, pp 359–366. https://doi.org/10.1109/ICEEE2019.2019.00076
4. Farooq A, Jia X, Zhou J (2019) Texture and shape features for grass weed classification using hyperspectral remote sensing images. In: IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pp 7208–7211. https://doi.org/10.1109/IGARSS.2019.8900132
5. Liyanage DC, Hudjakov R, Tamre M (2020) Hyperspectral imaging methods improve RGB image semantic segmentation of unstructured terrains. In: 2020 International Conference Mechatronic Systems and Materials (MSM) Bialystok, Poland, pp 1–5. https://doi.org/10.1109/MSM49833.2020.9201738
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