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Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Computers, Materials & Continua 2023年第2期74卷 3167-3181页

作者： Mesfer Al Duhayyim Hadeel Alsolai Siwar Ben Haj Hassine Jaber SAlzahrani Ahmed SSalama Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent *** remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic *** order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and ***,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on *** this motivation,this article presents a novel hurricane optimization algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing *** presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing *** accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature ***,Elman neural network(ENN)model is applied to allot proper class labels into the input ***,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this *** experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct *** comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

关键词： Hyperspectral images remote sensing deep learning hurricane optimization algorithm crop classification parameter tuning

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