Sultan Alahmari1, Saud Yonbawi2, Suneetha Racharla3, E. Laxmi Lydia4, Mohamad Khairi Ishak5, Hend Khalid Alkahtani6,*, Ayman Aljarbouh7, Samih M. Mostafa8
Computer Systems Science and Engineering, Vol.47, No.1, pp. 375-391, 2023, DOI: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… More >
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