Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction

Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial dimensions of features, strong data correlation, hig...
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Autor*in:	Hongda Li [verfasserIn] Jian Cui [verfasserIn] Xinle Zhang [verfasserIn] Yongqi Han [verfasserIn] Liying Cao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	hyperspectral images data dimensionality reduction feature extraction machine learning cell decomposition

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 18, p 4579
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:18, p 4579

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14184579

Katalog-ID:	DOAJ023316705

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authorswithroles_txt_mv	Hongda Li @@aut@@ Jian Cui @@aut@@ Xinle Zhang @@aut@@ Yongqi Han @@aut@@ Liying Cao @@aut@@
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author	Hongda Li
spellingShingle	Hongda Li misc hyperspectral images misc data dimensionality reduction misc feature extraction misc machine learning misc cell decomposition misc Science misc Q Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction
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topic_title	Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction hyperspectral images data dimensionality reduction feature extraction machine learning cell decomposition
topic	misc hyperspectral images misc data dimensionality reduction misc feature extraction misc machine learning misc cell decomposition misc Science misc Q
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title	Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction
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title_sort	dimensionality reduction and classification of hyperspectral remote sensing image feature extraction
title_auth	Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction
abstract	Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial dimensions of features, strong data correlation, high data redundancy, and long operation time, which lead to difficulty in image data classification. A data dimensionality reduction algorithm can transform the data into low-dimensional data with strong features and then classify the dimensionally reduced data. However, most classification methods cannot effectively extract dimensionality-reduced data features. In this paper, different dimensionality reduction and machine learning supervised classification algorithms are explored to determine a suitable combination method of dimensionality reduction and classification for hyperspectral images. Soft and hard classification methods are adopted to achieve the classification of pixels according to diversity. The results show that the data after dimensionality reduction retain the data features with high overall feature correlation, and the data dimension is drastically reduced. The dimensionality reduction method of unified manifold approximation and projection and the classification method of support vector machine achieve the best terrain classification with 99.57% classification accuracy. High-precision fitting of neural networks for soft classification of hyperspectral images with a model fitting correlation coefficient (R<sup<2</sup<) of up to 0.979 solves the problem of mixed pixel decomposition.
abstractGer	Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial dimensions of features, strong data correlation, high data redundancy, and long operation time, which lead to difficulty in image data classification. A data dimensionality reduction algorithm can transform the data into low-dimensional data with strong features and then classify the dimensionally reduced data. However, most classification methods cannot effectively extract dimensionality-reduced data features. In this paper, different dimensionality reduction and machine learning supervised classification algorithms are explored to determine a suitable combination method of dimensionality reduction and classification for hyperspectral images. Soft and hard classification methods are adopted to achieve the classification of pixels according to diversity. The results show that the data after dimensionality reduction retain the data features with high overall feature correlation, and the data dimension is drastically reduced. The dimensionality reduction method of unified manifold approximation and projection and the classification method of support vector machine achieve the best terrain classification with 99.57% classification accuracy. High-precision fitting of neural networks for soft classification of hyperspectral images with a model fitting correlation coefficient (R<sup<2</sup<) of up to 0.979 solves the problem of mixed pixel decomposition.
abstract_unstemmed	Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial dimensions of features, strong data correlation, high data redundancy, and long operation time, which lead to difficulty in image data classification. A data dimensionality reduction algorithm can transform the data into low-dimensional data with strong features and then classify the dimensionally reduced data. However, most classification methods cannot effectively extract dimensionality-reduced data features. In this paper, different dimensionality reduction and machine learning supervised classification algorithms are explored to determine a suitable combination method of dimensionality reduction and classification for hyperspectral images. Soft and hard classification methods are adopted to achieve the classification of pixels according to diversity. The results show that the data after dimensionality reduction retain the data features with high overall feature correlation, and the data dimension is drastically reduced. The dimensionality reduction method of unified manifold approximation and projection and the classification method of support vector machine achieve the best terrain classification with 99.57% classification accuracy. High-precision fitting of neural networks for soft classification of hyperspectral images with a model fitting correlation coefficient (R<sup<2</sup<) of up to 0.979 solves the problem of mixed pixel decomposition.
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title_short	Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction
url	https://doi.org/10.3390/rs14184579 https://doaj.org/article/9fb7fc81edef41dab49ff4fd8a053909 https://www.mdpi.com/2072-4292/14/18/4579 https://doaj.org/toc/2072-4292
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Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction

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