A review on graph-based semi-supervised learning methods for hyperspectral image classification

In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial information based graph semi-supervised classi...

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Main Authors: Shrutika S. Sawant, Manoharan Prabukumar
Format: Article
Language:English
Published: Elsevier 2020-08-01
Series:Egyptian Journal of Remote Sensing and Space Sciences
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1110982318301960
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author Shrutika S. Sawant
Manoharan Prabukumar
author_facet Shrutika S. Sawant
Manoharan Prabukumar
author_sort Shrutika S. Sawant
collection DOAJ
description In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial information based graph semi-supervised classification. In addition, related techniques are categorized into the following sub-types: (1) Manifold representation based Graph Semi-supervised Learning for HSI Classification (2) Sparse representation based Graph Semi-supervised Learning for HSI Classification. For each technique, methodologies, training and testing samples, various technical difficulties, as well as performances, are discussed. Additionally, future research challenges imposed by the graph-based model are indicated.
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spelling doaj.art-e0f95b7f4466488abab939267b38d2b22022-12-22T01:06:50ZengElsevierEgyptian Journal of Remote Sensing and Space Sciences1110-98232020-08-01232243248A review on graph-based semi-supervised learning methods for hyperspectral image classificationShrutika S. Sawant0Manoharan Prabukumar1SITE, VIT University, Vellore, IndiaCorresponding author.; SITE, VIT University, Vellore, IndiaIn this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial information based graph semi-supervised classification. In addition, related techniques are categorized into the following sub-types: (1) Manifold representation based Graph Semi-supervised Learning for HSI Classification (2) Sparse representation based Graph Semi-supervised Learning for HSI Classification. For each technique, methodologies, training and testing samples, various technical difficulties, as well as performances, are discussed. Additionally, future research challenges imposed by the graph-based model are indicated.http://www.sciencedirect.com/science/article/pii/S1110982318301960Hyperspectral imagesSemi-supervised learningImage classificationGraph-based learning
spellingShingle Shrutika S. Sawant
Manoharan Prabukumar
A review on graph-based semi-supervised learning methods for hyperspectral image classification
Egyptian Journal of Remote Sensing and Space Sciences
Hyperspectral images
Semi-supervised learning
Image classification
Graph-based learning
title A review on graph-based semi-supervised learning methods for hyperspectral image classification
title_full A review on graph-based semi-supervised learning methods for hyperspectral image classification
title_fullStr A review on graph-based semi-supervised learning methods for hyperspectral image classification
title_full_unstemmed A review on graph-based semi-supervised learning methods for hyperspectral image classification
title_short A review on graph-based semi-supervised learning methods for hyperspectral image classification
title_sort review on graph based semi supervised learning methods for hyperspectral image classification
topic Hyperspectral images
Semi-supervised learning
Image classification
Graph-based learning
url http://www.sciencedirect.com/science/article/pii/S1110982318301960
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