Review on graph learning for dimensionality reduction of hyperspectral image

Zhang, Liangpei; Luo, Fulin

doi:10.1080/10095020.2020.1720529

Cited by 25 publications

(20 citation statements)

References 52 publications

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“…The authors showed that their approach was effective in revealing the manifold structure for high-dimensional hyperspectral data, and their experimental results demonstrated classification results comparable to other state-of-the-art methods. Further information on graph-based learning approaches for hyperspectral information can be found in the survey paper by the authors in [81].…”

Section: B Big Data With Hyperspectral Analytics In Agriculturementioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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Hyperspectral and multispectral information processing systems and technologies have demonstrated its usefulness for the improvement of agricultural productivity and practices by providing useful information to farmers and crop managers on the factors affecting crop status and growth. These technologies are widely used in a range of agriculture applications such as crop management, crop yield forecasting, crop disease detection, and the monitoring of agriculture land usage, water, and soil conditions. Hyperspectral information sensing can acquire several hundred spectral bands that cover the electromagnetic spectrum of an observational scene in a single acquisition. The resulting hyperspectral data cube contains a large volume of spatial and spectral information. The hyperspectral sequence of images or video further increases the data generation velocity and volume which lead to the Big data challenges particularly in agricultural remote sensing applications. This paper is structured to first give a comprehensive review of representative studies to provide insights into significant research efforts in agriculture using Big data, machine learning and deep learning with the focus on frameworks or architectures, information processing and analytics with hyperspectral and multispectral data. The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising. The paper then further explores the potential of using ensemble machine learning and scalable parallel discriminant analysis which takes into consideration the spatial and spectral components for Big data in agriculture. To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported. Furthermore, the potential of ensemble machine learning and scalable parallel discriminant analysis has not been explored in agriculture information processing. Experiments and data analytics have been performed on hyperspectral data from agriculture for validation. The results have shown the good performance of our approach.

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Section: B Big Data With Hyperspectral Analytics In Agriculturementioning

confidence: 99%

Big Data and Machine Learning With Hyperspectral Information in Agriculture

2021

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“…Band selection approaches have been grouped into six namely ranking-based, searching-based, clustering-based, sparsity-based, embedding learning-based, and hybrid scheme-based methods [ 86 ]. Graph learning dimension reduction method has also been reported as an effective approach for analyzing intrinsic characteristics of hyperspectral imaging data [ 87 ]. The most widely used dimensionality reduction techniques, however, are the linear methods of principal component analysis (PCA) and multidimensional scaling (MDS).…”

Section: Hyperspectral Imaging Technology and Instrumentationmentioning

confidence: 99%

Non-Destructive Assessment of Chicken Egg Fertility

Adegbenjo

Liu

Ngadi

2020

Sensors

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Total hatching egg set (for both egg production chicks and broilers) in the Agriculture and Agri-Food Canada report 2017 was over 1.0 billion. With the fertility rate for this year observed to be around 82%, there were about 180 million unhatched eggs (worth over 300 million Canadian dollars) incubated in Canada for the year 2017 alone. These non-hatching (non-fertile) eggs can find useful applications as commercial table eggs or low-grade food stock if they can be detected early and isolated accordingly preferably prior to incubation. The conventional method of chicken egg fertility assessment termed candling, is subjective, cumbersome, slow, and eventually inefficient, leading to huge economic losses. Hence, there is a need for a non-destructive, fast and online prediction technology to assist with early chicken egg fertility identification problem. This paper reviewed existing non-destructive approaches including ultrasound and dielectric measurements, thermal imaging, machine vision, spectroscopy, and hyperspectral imaging. Hyperspectral imaging was extensively discussed, being an emerging new technology with great potential. Suggestions were finally proffered towards building futuristic robust model(s) for early detection of chicken egg fertility.

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“…Similarly, the development of high spatial resolution instruments installed on airborne and spaceborne platforms has resulted in an increase in applications of ML for special object detection [8]. ML also plays a vital role in dimensionality reduction [9] of hyperspectral images composed of many essential features for several scientific applications [10]. Other roles ML plays in RS applications include spectral unmixing, regression, image fusion, etc.…”

Section: Introductionmentioning

confidence: 99%

A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

Ghanbari

Mahdianpari

Homayouni

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Since the rise of deep learning in the past few years, convolutional neural networks (CNNs) have quickly found their place within the remote sensing (RS) community. As a result, they have transitioned away from other machine learning techniques, achieving unprecedented improvements in many specific RS applications. This paper presents a meta-analysis of 416 peer-reviewed journal articles, summarizes CNN advancements, and its current status under RS applications. The review process includes a statistical and descriptive analysis of a database comprised of 23 fields, including 1) general characteristics such as various applications, study objectives, sensors, and data types, and 2) algorithm specifications such as different types of CNN models, parameter settings, and reported accuracies. This review begins with a comprehensive survey of the relevant articles without considering any specific criteria to give readers an idea of general trends, then investigates CNNs within different RS applications to provide specific directions for the researchers. Finally, a conclusion summarizes potentialities, critical issues, and challenges related to the observed trends.

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Review on graph learning for dimensionality reduction of hyperspectral image

Cited by 25 publications

References 52 publications

Big Data and Machine Learning With Hyperspectral Information in Agriculture

Big Data and Machine Learning With Hyperspectral Information in Agriculture

Non-Destructive Assessment of Chicken Egg Fertility

A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

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