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Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

Sultan Alahmari1, Saud Yonbawi2, Suneetha Racharla3, E. Laxmi Lydia4, Mohamad Khairi Ishak5, Hend Khalid Alkahtani6,*, Ayman Aljarbouh7, Samih M. Mostafa8

1 King Abdulaziz City for Science and Technology, P.O Box 6086, Riyadh 11442, Saudi Arabia
2 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
3 Department of AIML, Aditya Engineering College, Surempallem, Andhra Pradesh, India
4 Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, 530049, India
5 School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia (USM), Nibong Tebal, Penang, 14300, Malaysia
6 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
7 Department of Computer Science, University of Central Asia, Naryn, 722600, Kyrgyzstan
8 Faculty of Computers and Information, South Valley University, Qena, 83523, Egypt

* Corresponding Author: Hend Khalid Alkahtani. Email: email

Computer Systems Science and Engineering 2023, 47(1), 375-391. https://doi.org/10.32604/csse.2023.036362

Abstract

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed scenes. Much spatial information and spectral signatures of hyperspectral images (HSIs) present greater potential for detecting and classifying fine crops. The accurate classification of crop kinds utilizing hyperspectral remote sensing imaging (RSI) has become an indispensable application in the agricultural domain. It is significant for the prediction and growth monitoring of crop yields. Amongst the deep learning (DL) techniques, Convolution Neural Network (CNN) was the best method for classifying HSI for their incredible local contextual modeling ability, enabling spectral and spatial feature extraction. This article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification (HMAODL-CTC) algorithm on HSI. The proposed HMAODL-CTC model mainly intends to categorize different types of crops on HSI. To accomplish this, the presented HMAODL-CTC model initially carries out image preprocessing to improve image quality. In addition, the presented HMAODL-CTC model develops dilated convolutional neural network (CNN) for feature extraction. For hyperparameter tuning of the dilated CNN model, the HMAO algorithm is utilized. Eventually, the presented HMAODL-CTC model uses an extreme learning machine (ELM) model for crop type classification. A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC algorithm. Extensive comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.

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APA Style
Alahmari, S., Yonbawi, S., Racharla, S., Lydia, E.L., Ishak, M.K. et al. (2023). Hybrid multi-strategy aquila optimization with deep learning driven crop type classification on hyperspectral images. Computer Systems Science and Engineering, 47(1), 375-391. https://doi.org/10.32604/csse.2023.036362
Vancouver Style
Alahmari S, Yonbawi S, Racharla S, Lydia EL, Ishak MK, Alkahtani HK, et al. Hybrid multi-strategy aquila optimization with deep learning driven crop type classification on hyperspectral images. Comput Syst Sci Eng. 2023;47(1):375-391 https://doi.org/10.32604/csse.2023.036362
IEEE Style
S. Alahmari et al., "Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images," Comput. Syst. Sci. Eng., vol. 47, no. 1, pp. 375-391. 2023. https://doi.org/10.32604/csse.2023.036362



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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