Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques

Two common techniques for classifying satellite imagery are pixel-based and Feature extraction image analysis methods. Typically, for agreements reached imaging, pixel-based analysis is used, whereas high-resolution imagery is suitable for Feature extraction analysis. However, In the classification...

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Autores principales:	Musleh Alaa Adnan, Jaber Hussein Sabah
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	envi land use land cover remote sensing pixel-based feature extraction gis Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/b8945e72f68b42e28afb765a62489858
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id	oai:doaj.org-article:b8945e72f68b42e28afb765a62489858
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spelling	oai:doaj.org-article:b8945e72f68b42e28afb765a624898582021-11-12T11:43:51ZComparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques2267-124210.1051/e3sconf/202131804007https://doaj.org/article/b8945e72f68b42e28afb765a624898582021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/94/e3sconf_icge2021_04007.pdfhttps://doaj.org/toc/2267-1242Two common techniques for classifying satellite imagery are pixel-based and Feature extraction image analysis methods. Typically, for agreements reached imaging, pixel-based analysis is used, whereas high-resolution imagery is suitable for Feature extraction analysis. However, In the classification of moderate images, image segmentation's ability depending on criteria such as shape, color, texture, and spatial features in Feature extraction image analysis implies it can perform better than pixel-based analysis. A comparative study of the two methods was performed using Sentinel-2 imagery from 18 May 2020 to categorize LU/LC in the City of Baghdad, Iraq. After calculating LU/LC for Baghdad images' capital, a supervised classification was performed using the two methods. The images used have been the support vector machines (SVM) and the maximum likelihood classification (MLC) for pixel-based method and Feature extraction method, which is available in ENVI and ArcGIS software packages, respectively. Land cover and land use classes included five Groups (vegetation area, asphalt road, soil area, water body, and built-up) was found that the Feature extraction methodology produced higher overall accuracy and Kappa index in the city of Baghdad image. The highest achieved accuracy for the Feature extraction technique was overall accuracy 95% with Kappa index 0.94 of SVM and overall accuracy of 92% with Kappa index 0.90 of MLC. In comparison, the highest accuracy for the pixel-based was overall accuracy 88% with Kappa index 0.84 of SVM and overall accuracy 86% with Kappa index 0.82 of MLC.Musleh Alaa AdnanJaber Hussein SabahEDP Sciencesarticleenviland useland coverremote sensingpixel-basedfeature extractiongisEnvironmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 318, p 04007 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	envi land use land cover remote sensing pixel-based feature extraction gis Environmental sciences GE1-350
spellingShingle	envi land use land cover remote sensing pixel-based feature extraction gis Environmental sciences GE1-350 Musleh Alaa Adnan Jaber Hussein Sabah Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques
description	Two common techniques for classifying satellite imagery are pixel-based and Feature extraction image analysis methods. Typically, for agreements reached imaging, pixel-based analysis is used, whereas high-resolution imagery is suitable for Feature extraction analysis. However, In the classification of moderate images, image segmentation's ability depending on criteria such as shape, color, texture, and spatial features in Feature extraction image analysis implies it can perform better than pixel-based analysis. A comparative study of the two methods was performed using Sentinel-2 imagery from 18 May 2020 to categorize LU/LC in the City of Baghdad, Iraq. After calculating LU/LC for Baghdad images' capital, a supervised classification was performed using the two methods. The images used have been the support vector machines (SVM) and the maximum likelihood classification (MLC) for pixel-based method and Feature extraction method, which is available in ENVI and ArcGIS software packages, respectively. Land cover and land use classes included five Groups (vegetation area, asphalt road, soil area, water body, and built-up) was found that the Feature extraction methodology produced higher overall accuracy and Kappa index in the city of Baghdad image. The highest achieved accuracy for the Feature extraction technique was overall accuracy 95% with Kappa index 0.94 of SVM and overall accuracy of 92% with Kappa index 0.90 of MLC. In comparison, the highest accuracy for the pixel-based was overall accuracy 88% with Kappa index 0.84 of SVM and overall accuracy 86% with Kappa index 0.82 of MLC.
format	article
author	Musleh Alaa Adnan Jaber Hussein Sabah
author_facet	Musleh Alaa Adnan Jaber Hussein Sabah
author_sort	Musleh Alaa Adnan
title	Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques
title_short	Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques
title_full	Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques
title_fullStr	Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques
title_full_unstemmed	Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques
title_sort	comparative analysis of feature extraction and pixel-based classification of high-resolution satellite images using geospatial techniques
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/b8945e72f68b42e28afb765a62489858
work_keys_str_mv	AT muslehalaaadnan comparativeanalysisoffeatureextractionandpixelbasedclassificationofhighresolutionsatelliteimagesusinggeospatialtechniques AT jaberhusseinsabah comparativeanalysisoffeatureextractionandpixelbasedclassificationofhighresolutionsatelliteimagesusinggeospatialtechniques
_version_	1718430640894902272

Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques

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