Gaussian bilateral filtered discrete Hartley feature transformation based infomax boosting for hyperspectral image classification

Hyperspectral Image (HSI) is a spatially sampled image collected from a lot of neighboring narrowed spectrum bands via hyperspectral sensor. The process of HSIs is a difficult obligation because of the high-dimensional image features. Conventional techniques mainly utilize hand-made characteristics...

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Autores principales:	Deepalakshmi Senthilkumar, Arulmurugan R
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2021
Materias:	Hyperspectral image classification Gaussian kernelized bilateral filter-based preprocessing Hartley transformation technique Infomax boost Image comprises Extreme learning machine Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/f2ecc5e5b8af4a459ae2bde5a638e495
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spelling	oai:doaj.org-article:f2ecc5e5b8af4a459ae2bde5a638e4952021-11-26T04:41:15ZGaussian bilateral filtered discrete Hartley feature transformation based infomax boosting for hyperspectral image classification2666-603010.1016/j.ijin.2021.11.001https://doaj.org/article/f2ecc5e5b8af4a459ae2bde5a638e4952021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666603021000257https://doaj.org/toc/2666-6030Hyperspectral Image (HSI) is a spatially sampled image collected from a lot of neighboring narrowed spectrum bands via hyperspectral sensor. The process of HSIs is a difficult obligation because of the high-dimensional image features. Conventional techniques mainly utilize hand-made characteristics that are not fit for classification. For the purpose of enhancing the correctness and reducing the overall time complexity, an efficient technique called Gaussian Bilateral Filtered Hartley Feature Transformation Based Infomax Boost Classification (GBFHFT-IBC) is introduced. The input hyperspectral image is divided into the number of spectral bands. The proposed GBFHFT-IBC method includes three fundamental procedures, which include preprocessing, characteristics classification and extraction. Preprocessing is carried out using a Gaussian kernelized bilateral filter that replaces the pixel intensity from the picture with the weighted to take the position of the intensity of every pixel from the picture with the prejudiced average of neighboring pixels resulting in it removes the noisy pixels and improve the image quality. After preprocessing, the different characteristics like texture, color and shape are extracted from the image using the Discrete Hartley transformation technique. With the extracted features, the Infomax Boost method is applicable when classifying different scenes from the spectrum band, which has to be grouped into various categories with the top categorization accuracy that consider the minimal error rates. The experiments are done using the hyperspectral picture dataset composed of various factors like categorization accuracy, PSNR, positive and negative rate, and timeline complexity considering the number of spectral bands. The observed result shows that the presented GBFHFT-IBC method improves the hyperspectral image classification correctness, PSNR and minimized negative and positive rates, which include timeline complexity compared to using the sophisticated techniques.Deepalakshmi SenthilkumarArulmurugan RKeAi Communications Co., Ltd.articleHyperspectral image classificationGaussian kernelized bilateral filter-based preprocessingHartley transformation techniqueInfomax boostImage comprisesExtreme learning machineElectronic computers. Computer scienceQA75.5-76.95ENInternational Journal of Intelligent Networks, Vol 2, Iss , Pp 195-203 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Hyperspectral image classification Gaussian kernelized bilateral filter-based preprocessing Hartley transformation technique Infomax boost Image comprises Extreme learning machine Electronic computers. Computer science QA75.5-76.95
spellingShingle	Hyperspectral image classification Gaussian kernelized bilateral filter-based preprocessing Hartley transformation technique Infomax boost Image comprises Extreme learning machine Electronic computers. Computer science QA75.5-76.95 Deepalakshmi Senthilkumar Arulmurugan R Gaussian bilateral filtered discrete Hartley feature transformation based infomax boosting for hyperspectral image classification
description	Hyperspectral Image (HSI) is a spatially sampled image collected from a lot of neighboring narrowed spectrum bands via hyperspectral sensor. The process of HSIs is a difficult obligation because of the high-dimensional image features. Conventional techniques mainly utilize hand-made characteristics that are not fit for classification. For the purpose of enhancing the correctness and reducing the overall time complexity, an efficient technique called Gaussian Bilateral Filtered Hartley Feature Transformation Based Infomax Boost Classification (GBFHFT-IBC) is introduced. The input hyperspectral image is divided into the number of spectral bands. The proposed GBFHFT-IBC method includes three fundamental procedures, which include preprocessing, characteristics classification and extraction. Preprocessing is carried out using a Gaussian kernelized bilateral filter that replaces the pixel intensity from the picture with the weighted to take the position of the intensity of every pixel from the picture with the prejudiced average of neighboring pixels resulting in it removes the noisy pixels and improve the image quality. After preprocessing, the different characteristics like texture, color and shape are extracted from the image using the Discrete Hartley transformation technique. With the extracted features, the Infomax Boost method is applicable when classifying different scenes from the spectrum band, which has to be grouped into various categories with the top categorization accuracy that consider the minimal error rates. The experiments are done using the hyperspectral picture dataset composed of various factors like categorization accuracy, PSNR, positive and negative rate, and timeline complexity considering the number of spectral bands. The observed result shows that the presented GBFHFT-IBC method improves the hyperspectral image classification correctness, PSNR and minimized negative and positive rates, which include timeline complexity compared to using the sophisticated techniques.
format	article
author	Deepalakshmi Senthilkumar Arulmurugan R
author_facet	Deepalakshmi Senthilkumar Arulmurugan R
author_sort	Deepalakshmi Senthilkumar
title	Gaussian bilateral filtered discrete Hartley feature transformation based infomax boosting for hyperspectral image classification
title_short	Gaussian bilateral filtered discrete Hartley feature transformation based infomax boosting for hyperspectral image classification
title_full	Gaussian bilateral filtered discrete Hartley feature transformation based infomax boosting for hyperspectral image classification
title_fullStr	Gaussian bilateral filtered discrete Hartley feature transformation based infomax boosting for hyperspectral image classification
title_full_unstemmed	Gaussian bilateral filtered discrete Hartley feature transformation based infomax boosting for hyperspectral image classification
title_sort	gaussian bilateral filtered discrete hartley feature transformation based infomax boosting for hyperspectral image classification
publisher	KeAi Communications Co., Ltd.
publishDate	2021
url	https://doaj.org/article/f2ecc5e5b8af4a459ae2bde5a638e495
work_keys_str_mv	AT deepalakshmisenthilkumar gaussianbilateralfiltereddiscretehartleyfeaturetransformationbasedinfomaxboostingforhyperspectralimageclassification AT arulmuruganr gaussianbilateralfiltereddiscretehartleyfeaturetransformationbasedinfomaxboostingforhyperspectralimageclassification
_version_	1718409817485213696

Gaussian bilateral filtered discrete Hartley feature transformation based infomax boosting for hyperspectral image classification

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