Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data

In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all ar...

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Autores principales:	Ling Dai, Guangyun Zhang, Jinqi Gong, Rongting Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hyperspectral image feature extraction interactive features sparse multiclass logistic regression autonomous learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/a5ce9725cad344579a28a8de22c05398
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id	oai:doaj.org-article:a5ce9725cad344579a28a8de22c05398
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spelling	oai:doaj.org-article:a5ce9725cad344579a28a8de22c053982021-11-11T15:26:12ZAutonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data10.3390/app1121105022076-3417https://doaj.org/article/a5ce9725cad344579a28a8de22c053982021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10502https://doaj.org/toc/2076-3417In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.Ling DaiGuangyun ZhangJinqi GongRongting ZhangMDPI AGarticlehyperspectral imagefeature extractioninteractive featuressparse multiclass logistic regressionautonomous learningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10502, p 10502 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hyperspectral image feature extraction interactive features sparse multiclass logistic regression autonomous learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	hyperspectral image feature extraction interactive features sparse multiclass logistic regression autonomous learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Ling Dai Guangyun Zhang Jinqi Gong Rongting Zhang Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
description	In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.
format	article
author	Ling Dai Guangyun Zhang Jinqi Gong Rongting Zhang
author_facet	Ling Dai Guangyun Zhang Jinqi Gong Rongting Zhang
author_sort	Ling Dai
title	Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
title_short	Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
title_full	Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
title_fullStr	Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
title_full_unstemmed	Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data
title_sort	autonomous learning interactive features for hyperspectral remotely sensed data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a5ce9725cad344579a28a8de22c05398
work_keys_str_mv	AT lingdai autonomouslearninginteractivefeaturesforhyperspectralremotelysenseddata AT guangyunzhang autonomouslearninginteractivefeaturesforhyperspectralremotelysenseddata AT jinqigong autonomouslearninginteractivefeaturesforhyperspectralremotelysenseddata AT rongtingzhang autonomouslearninginteractivefeaturesforhyperspectralremotelysenseddata
_version_	1718435326043619328

Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data

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