Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral Images-Reference-Cited by-同舟云学术

Intelligent Sine Cosine Optimization with Deep Transfer Learning Based Crops Type Classification Using Hyperspectral Images

Published:2022-06-22 Issue:5 Volume:48 Page:621-632
ISSN:0703-8992
Container-title:Canadian Journal of Remote Sensing
language:en
Short-container-title:Canadian Journal of Remote Sensing

Author:

Escorcia-Gutierrez José¹²,Gamarra Margarita³,Torres-Torres Melitsa⁴,Madera Natasha⁵,Calabria-Sarmiento Juan C.⁶⁷,Mansour Romany F.⁸

Affiliation:

1. Biomedical Engineering Program, Corporación Universitaria Reformada, Barranquilla 080001, Colombia

2. Research Center - CIENS, Escuela Naval de Suboficiales A.R.C. "Barranquilla", Barranquilla 080001, Colombia

3. Departament of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla 08001, Colombia

4. Research Group IET-UAC, Universidad Autónoma del Caribe, Barranquilla 08001, Colombia

5. Electronics and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla 080001, Colombia

6. Department of Computer Science, Universidad Simon Bolivar, Barranquilla 080001, Colombia

7. Doctorate in Computer Science, Universidad del Norte, Barranquilla 080001, Colombia

8. Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/07038992.2022.2081538

Reference26 articles.

1. Advances in Sine Cosine Algorithm: A comprehensive survey

2. Big Data and Machine Learning With Hyperspectral Information in Agriculture

3. Bernardo, L.S., Damaševičius, R., de Albuquerque, V.H.C., and Maskeliūnas, R. 2021. “A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns.” Advanced Machine Learning Techniques in Data Analysis. Vol. 31: pp. 549–561.

4. Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images

5. Educating the Next Generation of Remote Sensing Specialists: Skills and Industry Needs in a Changing World

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