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Deep learning techniques for hyperspectral image analysis in agriculture: A review

In recent years, there has been a growing emphasis on assessing and ensuring the quality of horticultural and agricultural produce. Traditional methods involving field measurements, investigations, and statistical analyses are labour-intensive, time-consuming, and costly. As a solution, Hyperspectral Imaging (HSI) has emerged as a non-destructive and environmentally friendly technology. HSI has gained significant popularity as a new technology, particularly for its promising applications in remote sensing, notably in agriculture. However, classifying HSI data is highly complex because it involves several challenges, such as the excessive redundancy of spectral bands, scarcity of training samples, and the intricate non-linear relationship between spatial positions and spectral bands. Notably, Deep Learning (DL) techniques have demonstrated remarkable efficacy in various HSI analysis tasks, including those within agriculture. As interest continues to surge in leveraging HSI data for agricultural applications through DL approaches, a pressing need exists for a comprehensive survey that can effectively navigate researchers through the significant strides achieved and the future promising research directions in this domain. This literature review diligently compiles, analyzes, and discusses recent endeavours employing DL methodologies. These methodologies encompass a spectrum of approaches, ranging from Autoencoders (AE) to Convolutional Neural Networks (CNN) (in 1D, 2D, and 3D configurations), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), Generative Adversarial Networks (GAN), Transfer Learning (TL), Semi-Supervised Learning (SSL), Few-Shot Learning (FSL) and Active Learning (AL). These approaches are tailored to address the unique challenges posed by agricultural HSI analysis. This review evaluates and discusses the performance exhibited by these diverse approaches. To this end, the efficiency of these approaches has been rigorously analyzed and discussed based on the results of the state-of-the-art papers on widely recognized land cover datasets.

Availability
53621.3678Perpustakaan BIG (Eksternal Harddisk)Available
Detail Information
Series Title
ISPRS Open Journal of Photogrammetry and Remote Sensing
Call Number
621.3678
Publisher
Amsterdam : Elsevier.,
Collation
19 hlm PDF, 3.154 KB
Language
Inggris
ISBN/ISSN
1872-8235
Classification
621.3678
Content Type
text
Media Type
-
Carrier Type
-
Edition
Vol.12, April 2024
Subject(s)
Deep learning
Hyperspectral imaging
HIS
Agriculture
CNN
RNN
GAN
Specific Detail Info
-
Statement of Responsibility
Other version/related

No other version available

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  • Deep learning techniques for hyperspectral image analysis in agriculture: A review
    In recent years, there has been a growing emphasis on assessing and ensuring the quality of horticultural and agricultural produce. Traditional methods involving field measurements, investigations, and statistical analyses are labour-intensive, time-consuming, and costly. As a solution, Hyperspectral Imaging (HSI) has emerged as a non-destructive and environmentally friendly technology. HSI has gained significant popularity as a new technology, particularly for its promising applications in remote sensing, notably in agriculture. However, classifying HSI data is highly complex because it involves several challenges, such as the excessive redundancy of spectral bands, scarcity of training samples, and the intricate non-linear relationship between spatial positions and spectral bands. Notably, Deep Learning (DL) techniques have demonstrated remarkable efficacy in various HSI analysis tasks, including those within agriculture. As interest continues to surge in leveraging HSI data for agricultural applications through DL approaches, a pressing need exists for a comprehensive survey that can effectively navigate researchers through the significant strides achieved and the future promising research directions in this domain. This literature review diligently compiles, analyzes, and discusses recent endeavours employing DL methodologies. These methodologies encompass a spectrum of approaches, ranging from Autoencoders (AE) to Convolutional Neural Networks (CNN) (in 1D, 2D, and 3D configurations), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), Generative Adversarial Networks (GAN), Transfer Learning (TL), Semi-Supervised Learning (SSL), Few-Shot Learning (FSL) and Active Learning (AL). These approaches are tailored to address the unique challenges posed by agricultural HSI analysis. This review evaluates and discusses the performance exhibited by these diverse approaches. To this end, the efficiency of these approaches has been rigorously analyzed and discussed based on the results of the state-of-the-art papers on widely recognized land cover datasets.
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