Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images-Reference-Cited by-同舟云学术

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Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images

Published:2020 Issue:5 Volume:5 Page:652-659
ISSN:2415-6698
Container-title:Advances in Science, Technology and Engineering Systems Journal
language:
Short-container-title:Adv. sci. technol. eng. syst. j.

Author:

AlAfandy Khalid A.,Hicham Hicham,Lazaar Mohamed,Achhab Mohammed Al

Publisher

ASTES Journal

Subject

Management of Technology and Innovation,Physics and Astronomy (miscellaneous),Engineering (miscellaneous)

Link

https://www.astesj.com/publications/ASTESJ_050580.pdf

Reference17 articles.

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2. K. Hwan Kim and S. June Kim, "Neural Spike Sorting Under Nearly 0-dB Signal-to-Noise Ratio Using Nonlinear Energy Operator and Artificial Neural-Network Classifier", IEEE Transactions on Medical Engineering, 47(10), 1406-1411, 2000. http://doi.org/10.1109/10.871415

3. M. Elhenawy and H. Rakha, "Spatiotemporal Traffic State Prediction Based on Discriminatively Pre-trained Deep Neural Networks", Advances in Science, Technology and Engineering Systems Journal (ASTESJ), 2(3), 678-686, 2017. http://dx.doi.org/10.25046/aj020387

4. M. Nair S., and Bindhu J.S., "Supervised Techniques and Approaches for Satellite Image Classification", International Journal of Computer Applications (IJCA), 134(16), 1-6, 2016. http://doi.org/10.5120/ijca2016908202

5. W. Rawat, and Z. Wang, "Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review", Neural computation, 29(9), 2352-2449, 2017. http://doi.org/10.1162/NECO_a_00990

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