Extraction of forest plantation extents using majority voting classification fusion algorithm
Satellite Phased Array L-band Synthetic Aperture Radar-2 has great advantages in extracting natural and industrial forest plantation in tropical areas, but it suffers from presence of speckle that create problem to identify the forest body. Optimal fusion of Landsat-8 operational land imager bands w...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/67018/ http://psasir.upm.edu.my/id/eprint/67018/1/39TH%20ACRS-5.pdf |
| _version_ | 1848855733000470528 |
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| author | Saharkhiz, Maryam Adel Pradhan, Biswajeet Rizeei, Hossein Mojaddadi Mohamed Shariff, Abdul Rashid |
| author_facet | Saharkhiz, Maryam Adel Pradhan, Biswajeet Rizeei, Hossein Mojaddadi Mohamed Shariff, Abdul Rashid |
| author_sort | Saharkhiz, Maryam Adel |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Satellite Phased Array L-band Synthetic Aperture Radar-2 has great advantages in extracting natural and industrial forest plantation in tropical areas, but it suffers from presence of speckle that create problem to identify the forest body. Optimal fusion of Landsat-8 operational land imager bands with ALOS PALSAR-2 can provide the ideal complementary information for an accurate forest extraction while suppressing unwanted information. The goal of this study is to analyze the potential ability of Landsat-8 OLI and ALOS PALSAR-2 as complementary data resources in order to extract land cover especially forest types. Comprehensive preprocessing analysis (e.g. geometric correction, filtering enhancement and polarization combination) were conducted on ALOS PALSAR-2 dataset in order to make the imagery ready for processing. Principal component index method as one of the most effective Pan-Sharpening fusion approaches was used to synthesize Landsat and ALOS PALSAR-2 images. Three different classifiers methods (support vector machine, k-nearest neighborhood, and random forest) were employed and then fused by majority voting algorithm to generate more robust and precise classification result. Accuracy of the final fused result was assessed on the basis of ground truth points by using confusion matrices and kappa coefficient. This study proves that the accurate and reliable majority voting fusion method can be used to extract large-scale land cover with emphasis on natural and industrial forest plantation from synthetic aperture radar and optical datasets. |
| first_indexed | 2025-11-15T11:30:27Z |
| format | Conference or Workshop Item |
| id | upm-67018 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:30:27Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-670182019-03-06T05:38:33Z http://psasir.upm.edu.my/id/eprint/67018/ Extraction of forest plantation extents using majority voting classification fusion algorithm Saharkhiz, Maryam Adel Pradhan, Biswajeet Rizeei, Hossein Mojaddadi Mohamed Shariff, Abdul Rashid Satellite Phased Array L-band Synthetic Aperture Radar-2 has great advantages in extracting natural and industrial forest plantation in tropical areas, but it suffers from presence of speckle that create problem to identify the forest body. Optimal fusion of Landsat-8 operational land imager bands with ALOS PALSAR-2 can provide the ideal complementary information for an accurate forest extraction while suppressing unwanted information. The goal of this study is to analyze the potential ability of Landsat-8 OLI and ALOS PALSAR-2 as complementary data resources in order to extract land cover especially forest types. Comprehensive preprocessing analysis (e.g. geometric correction, filtering enhancement and polarization combination) were conducted on ALOS PALSAR-2 dataset in order to make the imagery ready for processing. Principal component index method as one of the most effective Pan-Sharpening fusion approaches was used to synthesize Landsat and ALOS PALSAR-2 images. Three different classifiers methods (support vector machine, k-nearest neighborhood, and random forest) were employed and then fused by majority voting algorithm to generate more robust and precise classification result. Accuracy of the final fused result was assessed on the basis of ground truth points by using confusion matrices and kappa coefficient. This study proves that the accurate and reliable majority voting fusion method can be used to extract large-scale land cover with emphasis on natural and industrial forest plantation from synthetic aperture radar and optical datasets. 2018 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/67018/1/39TH%20ACRS-5.pdf Saharkhiz, Maryam Adel and Pradhan, Biswajeet and Rizeei, Hossein Mojaddadi and Mohamed Shariff, Abdul Rashid (2018) Extraction of forest plantation extents using majority voting classification fusion algorithm. In: 39th Asian Conference on Remote Sensing (ACRS 2018), 15-19 Oct. 2018, Renaissance Kuala Lumpur Hotel, Malaysia. (pp. 1971-1979). |
| spellingShingle | Saharkhiz, Maryam Adel Pradhan, Biswajeet Rizeei, Hossein Mojaddadi Mohamed Shariff, Abdul Rashid Extraction of forest plantation extents using majority voting classification fusion algorithm |
| title | Extraction of forest plantation extents using majority voting classification fusion algorithm |
| title_full | Extraction of forest plantation extents using majority voting classification fusion algorithm |
| title_fullStr | Extraction of forest plantation extents using majority voting classification fusion algorithm |
| title_full_unstemmed | Extraction of forest plantation extents using majority voting classification fusion algorithm |
| title_short | Extraction of forest plantation extents using majority voting classification fusion algorithm |
| title_sort | extraction of forest plantation extents using majority voting classification fusion algorithm |
| url | http://psasir.upm.edu.my/id/eprint/67018/ http://psasir.upm.edu.my/id/eprint/67018/1/39TH%20ACRS-5.pdf |