Classification of Hyperspectral Images Using Convolutional Neural Networks

Kozik, V. I.; Nezhevenko, E. S.

doi:10.15372/aut20210202

Cited by 1 publication

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“…In recent times, as a significant part of machine learning, deep learning (DL) has gained considerable attention as a result of its stronger abilities in feature extraction and analysis [8]. By extracting features of the input dataset from the bottom to the top of the networks, DL model forms the higher-level abstract feature appropriate for pattern categorization [9].…”

Section: Introductionmentioning

confidence: 99%

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Duhayyim¹,

Alsolai²,

Hassine³

et al. 2023

Computers, Materials &Amp; Continua

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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 images. Hyperspectral 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 spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of 3168 CMC, 2023, vol.74, no.2 the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

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Section: Introductionmentioning

confidence: 99%