A comparative analysis of various activation functions and optimizers in a convolutional neural network for hyperspectral image classification
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17546-5.pdf
Reference75 articles.
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2. Dave K, Vyas T, Trivedi YN (2022) A sample selection method based on similarity measure and fuzziness for crop classification from hyperspectral data. Adv Space Res. https://doi.org/10.1016/j.asr.2022.06.033
3. Khan MJ, Khan HS, Yousaf A, Khurshid K, Abbas A (2018) Modern Trends in Hyperspectral Image Analysis: A Review. IEEE Access 6:14118–14129. https://doi.org/10.1109/ACCESS.2018.2812999
4. Stuart MB, McGonigle AJ, Willmott JR (2019) Hyperspectral imaging in environmental monitoring: a review of recent developments and technological advances in compact field deployable systems. Sensors 19(14):3071. https://doi.org/10.3390/s19143071
5. Adão T, Hruška J, Pádua L, Bessa J, Peres E, Morais R, Sousa JJ (2017) Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing 9(11):1110. https://doi.org/10.3390/rs9111110
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