Low Complexity and Low Memory Compression Algorithm for Hyperspectral Image Sensors
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-023-10455-8.pdf
Reference57 articles.
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2. Verma, B., Prasad, R., Srivastava, P. K., Yadav, S. A., Singh, P., & Singh, R. K. (2022). Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms. Computers and Electronics in Agriculture., 192, 106581. https://doi.org/10.1016/j.compag.2021.106581
3. Siddiqui, A., Chauhan, P., Kumar, V., Jain, G., Deshmukh, A., & Kumar, P. (2022). Characterization of urban materials in AVIRIS-NG data using a mixture tuned matched filtering (MTMF) approach. Geocarto International., 37(1), 332–347.
4. Patel, A. K., Ghosh, J. K., & Sayyad, S. U. (2022). Fractional abundances study of macronutrients in soil using hyperspectral remote sensing. Geocarto International., 37(2), 474–493.
5. Constans, Y., Fabre, S., Seymour, M., Crombez, V., Deville, Y., & Briottet, X. (2022). Hyperspectral pansharpening in the reflective domain with a second panchromatic channel in the SWIR II spectral domain. Remote Sensing., 14(1), 113. https://doi.org/10.3390/rs14010113
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