Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran
Due to rapid population growth over recent decades, changes of urban areas have significantly impacted the environment. Urban is a heterogeneous and highly fragmented environment which has made them a challenging area for remote sensing imagery. The reliability of the information delivered by remote...
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| Format: | Article |
| Language: | English |
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İstanbul University
2016-01-01
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| Series: | İstanbul Üniversitesi Orman Fakültesi Dergisi |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.17099/jffiu.01280 |
| _version_ | 1797905548169969664 |
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| author | Ahmad Maleknezhad Yazdi Vahid Eisavi Ali Shahsavari |
| author_facet | Ahmad Maleknezhad Yazdi Vahid Eisavi Ali Shahsavari |
| author_sort | Ahmad Maleknezhad Yazdi |
| collection | DOAJ |
| description | Due to rapid population growth over recent decades, changes of urban areas have significantly impacted the environment. Urban is a heterogeneous and highly fragmented environment which has made them a challenging area for remote sensing imagery. The reliability of the information delivered by remote sensing applications in urban area highly depends on the quality of spatial and spectral data. Accordingly, the objective of this study is to analyze the impact of incorporation of Hyperion imagery and textural characteristics of high resolution panchromatic ALI imagery in classifying of urban region of south west of Tehran. To this end, we extracted textural information from panchromatic ALI imagery using gray-level co-occurrence matrix (GLCM) method. Classification was carried out by SVM method in five scenarios: Classification of spectral band of CNT method, classification of spectral bands plus texture with window size 3, size 5, size 7 and size 9. The classification results show that the urban areas of south west of Tehran are insufficiently characterized by the Hyperion satellite imagery. A quantitative assessment of the results demonstrated that the use of texture information improved urban land covers classification. As a result, combining of texture information with Hyperion imagery decreases class confusion specifically in heterogonous classes. The GLCM features show great potential for land use cover classification in heterogeneous areas with rich textural information. |
| first_indexed | 2024-04-10T10:07:03Z |
| format | Article |
| id | doaj.art-b39140e58a764b9fbe274220b3e454f7 |
| institution | Directory Open Access Journal |
| issn | 0535-8418 0535-8418 |
| language | English |
| last_indexed | 2024-04-10T10:07:03Z |
| publishDate | 2016-01-01 |
| publisher | İstanbul University |
| record_format | Article |
| series | İstanbul Üniversitesi Orman Fakültesi Dergisi |
| spelling | doaj.art-b39140e58a764b9fbe274220b3e454f72023-02-15T16:22:24Zengİstanbul Universityİstanbul Üniversitesi Orman Fakültesi Dergisi0535-84180535-84182016-01-016619010310.17099/jffiu.01280Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, IranAhmad Maleknezhad Yazdi0Vahid Eisavi1Ali Shahsavari2Tarbiat Modares University, Remote Sensing and GIS Department, M.Sc. in Remote Sensing, Tehran, IranTarbiat Modares University, Remote Sensing and GIS Department, M.Sc. in Remote Sensing, Tehran, IranTehran University, Remote Sensing and GIS Department, M.Sc. in Remote Sensing, Tehran, IranDue to rapid population growth over recent decades, changes of urban areas have significantly impacted the environment. Urban is a heterogeneous and highly fragmented environment which has made them a challenging area for remote sensing imagery. The reliability of the information delivered by remote sensing applications in urban area highly depends on the quality of spatial and spectral data. Accordingly, the objective of this study is to analyze the impact of incorporation of Hyperion imagery and textural characteristics of high resolution panchromatic ALI imagery in classifying of urban region of south west of Tehran. To this end, we extracted textural information from panchromatic ALI imagery using gray-level co-occurrence matrix (GLCM) method. Classification was carried out by SVM method in five scenarios: Classification of spectral band of CNT method, classification of spectral bands plus texture with window size 3, size 5, size 7 and size 9. The classification results show that the urban areas of south west of Tehran are insufficiently characterized by the Hyperion satellite imagery. A quantitative assessment of the results demonstrated that the use of texture information improved urban land covers classification. As a result, combining of texture information with Hyperion imagery decreases class confusion specifically in heterogonous classes. The GLCM features show great potential for land use cover classification in heterogeneous areas with rich textural information.http://dx.doi.org/10.17099/jffiu.01280Hyperspectral imageryimage textureGLCMremote sensingSVM classification. |
| spellingShingle | Ahmad Maleknezhad Yazdi Vahid Eisavi Ali Shahsavari Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran İstanbul Üniversitesi Orman Fakültesi Dergisi Hyperspectral imagery image texture GLCM remote sensing SVM classification. |
| title | Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran |
| title_full | Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran |
| title_fullStr | Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran |
| title_full_unstemmed | Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran |
| title_short | Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran |
| title_sort | incorporation of hyperspectral imagery and texture information in a svm method for classifying urban area of southern regions of tehran iran |
| topic | Hyperspectral imagery image texture GLCM remote sensing SVM classification. |
| url | http://dx.doi.org/10.17099/jffiu.01280 |
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