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

作     者：Sultan Alahmari Saud Yonbawi Suneetha Racharla ELaxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa 

作者机构：King Abdulaziz City for Science and TechnologyP.O Box 6086Riyadh 11442Saudi Arabia Department of Software EngineeringCollege of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of AIMLAditya Engineering CollegeSurempallemAndhra PradeshIndia Department of Computer Science and EngineeringVignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic EngineeringEngineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information SystemsCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer ScienceUniversity of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and InformationSouth Valley UniversityQena83523Egypt 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第47卷第10期

页      面：375-391页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R384) Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia 

主　　题：Crop type classification hyperspectral images agricultural monitoring deep learning metaheuristics 

摘      要：Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed *** spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine *** accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural *** is significant for the prediction and growth monitoring of crop *** 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 *** article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm *** proposed HMAODL-CTC model mainly intends to categorize different types of crops on *** accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image *** addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature *** hyperparameter tuning of the dilated CNN model,the HMAO algorithm is ***,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 *** comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.