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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| Format: | Article |
| Language: | English |
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EDP Sciences
2021-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/94/e3sconf_icge2021_04007.pdf |
| _version_ | 1819044672152010752 |
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| author | Musleh Alaa Adnan Jaber Hussein Sabah |
| author_facet | Musleh Alaa Adnan Jaber Hussein Sabah |
| author_sort | Musleh Alaa Adnan |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-21T10:16:24Z |
| format | Article |
| id | doaj.art-b8945e72f68b42e28afb765a62489858 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-12-21T10:16:24Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-b8945e72f68b42e28afb765a624898582022-12-21T19:07:35ZengEDP SciencesE3S Web of Conferences2267-12422021-01-013180400710.1051/e3sconf/202131804007e3sconf_icge2021_04007Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial TechniquesMusleh Alaa Adnan0Jaber Hussein Sabah1Surveying Engineering Department, University of BaghdadSurveying Engineering Department, University of BaghdadTwo 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/94/e3sconf_icge2021_04007.pdfenviland useland coverremote sensingpixel-basedfeature extractiongis |
| spellingShingle | Musleh Alaa Adnan Jaber Hussein Sabah Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques E3S Web of Conferences envi land use land cover remote sensing pixel-based feature extraction gis |
| title | 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_short | 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 |
| topic | envi land use land cover remote sensing pixel-based feature extraction gis |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/94/e3sconf_icge2021_04007.pdf |
| work_keys_str_mv | AT muslehalaaadnan comparativeanalysisoffeatureextractionandpixelbasedclassificationofhighresolutionsatelliteimagesusinggeospatialtechniques AT jaberhusseinsabah comparativeanalysisoffeatureextractionandpixelbasedclassificationofhighresolutionsatelliteimagesusinggeospatialtechniques |