ASSESSMENT OF NORMALIZATION TECHNIQUES ON THE ACCURACY OF HYPERSPECTRAL DATA CLUSTERING

10 citations as recorded by crossref.

1. Deep Self-Paced Residual Network for Multispectral Images Classification Based on Feature-Level Fusion J. Zhang et al. https://doi.org/10.1109/LGRS.2018.2854847
2. A Survey on the Datasets and Algorithms for Satellite Data Applications M. Affek & J. Szymański https://doi.org/10.1109/JSTARS.2024.3424954
3. Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects C. Nansen et al. https://doi.org/10.1186/s13007-022-00912-z
4. Evaluation of Postharvest Senescence of Broccoli via Hyperspectral Imaging X. Guo et al. https://doi.org/10.34133/2022/9761095
5. Improved chi-square feature selection for robust heart disease data classification H. Nayl et al. https://doi.org/10.3934/math.2026108
6. Deep learning-based hyperspectral microscopic imaging for cholangiocarcinoma detection and classification S. Kumar et al. https://doi.org/10.1364/OPTCON.527576
7. Normalization and outlier removal in class center-based firefly algorithm for missing value imputation H. Nugroho et al. https://doi.org/10.1186/s40537-021-00518-7
8. Segmentation of Microscopic Images of Pseudomonas Biofilms Using Deep Learning and Semantic Filtering V. Pavlova et al. https://doi.org/10.1134/S1054661826700586
9. Hyperspectral assessment of road pavement condition using machine learning: a comparative study with EnMAP and PRISMA data H. Haghighi et al. https://doi.org/10.1080/01431161.2026.2637846
10. Data normalization in machine learning V. Starovoitov & Y. Golub https://doi.org/10.37661/1816-0301-2021-18-3-83-96