Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping
Increasing the accuracy of thematic maps generated using satellite imagery is a crucial task in remote sensing. In this study, a product-level fusion (PLF) approach based on integration of different land-type maps generated using various satellite-derived indices including normalized difference wate...
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| Format: | Article |
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Springer Verlag
2015
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| _version_ | 1796860440033099776 |
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| author | Hazini, Sharifeh Hashim, Mazlan |
| author_facet | Hazini, Sharifeh Hashim, Mazlan |
| author_sort | Hazini, Sharifeh |
| collection | ePrints |
| description | Increasing the accuracy of thematic maps generated using satellite imagery is a crucial task in remote sensing. In this study, a product-level fusion (PLF) approach based on integration of different land-type maps generated using various satellite-derived indices including normalized difference water index (NDWI), normalized difference built-up index (NDBI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI) is proposed to improve the accuracy of land cover mapping. The suitability of the proposed approach for land cover mapping is evaluated in comparison with two high-performance image classification techniques including support vector machine (SVM) and artificial neural network (ANN). The results show that the overall accuracy and kappa values of about 95.95 % and 0.95, 94.91 % and 0.94, and 85.32 % and 0.82 are achieved for the PLF, SVM, and ANN approaches, respectively. The results indicate superiority of the PLF approach than SVM and ANN techniques for land cover classification of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery, especially for the extraction of forest, rice, and citrus classes. However, SVM technique also provided reliable result for land cover mapping. |
| first_indexed | 2024-03-05T19:41:23Z |
| format | Article |
| id | utm.eprints-58092 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T19:41:23Z |
| publishDate | 2015 |
| publisher | Springer Verlag |
| record_format | dspace |
| spelling | utm.eprints-580922022-04-07T02:58:06Z http://eprints.utm.my/58092/ Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping Hazini, Sharifeh Hashim, Mazlan G109.5 Global Positioning System Increasing the accuracy of thematic maps generated using satellite imagery is a crucial task in remote sensing. In this study, a product-level fusion (PLF) approach based on integration of different land-type maps generated using various satellite-derived indices including normalized difference water index (NDWI), normalized difference built-up index (NDBI), enhanced vegetation index (EVI), and normalized difference vegetation index (NDVI) is proposed to improve the accuracy of land cover mapping. The suitability of the proposed approach for land cover mapping is evaluated in comparison with two high-performance image classification techniques including support vector machine (SVM) and artificial neural network (ANN). The results show that the overall accuracy and kappa values of about 95.95 % and 0.95, 94.91 % and 0.94, and 85.32 % and 0.82 are achieved for the PLF, SVM, and ANN approaches, respectively. The results indicate superiority of the PLF approach than SVM and ANN techniques for land cover classification of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery, especially for the extraction of forest, rice, and citrus classes. However, SVM technique also provided reliable result for land cover mapping. Springer Verlag 2015 Article PeerReviewed Hazini, Sharifeh and Hashim, Mazlan (2015) Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping. Arabian Journal of Geosciences, 8 (11). pp. 9763-9773. ISSN 1866-7511 http://dx.doi.org/10.1007/s12517-015-1915-3 DOI:10.1007/s12517-015-1915-3 |
| spellingShingle | G109.5 Global Positioning System Hazini, Sharifeh Hashim, Mazlan Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping |
| title | Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping |
| title_full | Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping |
| title_fullStr | Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping |
| title_full_unstemmed | Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping |
| title_short | Comparative analysis of product-level fusion, support vector machine, and artificial neural network approaches for land cover mapping |
| title_sort | comparative analysis of product level fusion support vector machine and artificial neural network approaches for land cover mapping |
| topic | G109.5 Global Positioning System |
| work_keys_str_mv | AT hazinisharifeh comparativeanalysisofproductlevelfusionsupportvectormachineandartificialneuralnetworkapproachesforlandcovermapping AT hashimmazlan comparativeanalysisofproductlevelfusionsupportvectormachineandartificialneuralnetworkapproachesforlandcovermapping |