Imaging spectroscopy and light detection and ranging data fusion for urban features extraction
This study presents our findings on the fusion of Imaging Spectroscopy (IS) and LiDAR data for urban feature extraction. We carried out necessary preprocessing of the hyperspectral image. Minimum Noise Fraction (MNF) transforms was used for ordering hyperspectral bands according to their noise. Ther...
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| Format: | Article |
| Language: | English |
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Science Publications
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/28669/ http://psasir.upm.edu.my/id/eprint/28669/1/ajassp.2013.1575.1585.pdf |
| _version_ | 1848846181084430336 |
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| author | Idrees, Mohammed Mohd Shafri, Helmi Zulhaidi Saeidi, Vahideh |
| author_facet | Idrees, Mohammed Mohd Shafri, Helmi Zulhaidi Saeidi, Vahideh |
| author_sort | Idrees, Mohammed |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This study presents our findings on the fusion of Imaging Spectroscopy (IS) and LiDAR data for urban feature extraction. We carried out necessary preprocessing of the hyperspectral image. Minimum Noise Fraction (MNF) transforms was used for ordering hyperspectral bands according to their noise. Thereafter, we employed Optimum Index Factor (OIF) to statistically select the three most appropriate bands combination from MNF result. The composite image was classified using unsupervised classification (k-mean algorithm) and the accuracy of the classification assessed. Digital Surface Model (DSM) and LiDAR intensity were generated from the LiDAR point cloud. The LiDAR intensity was filtered to remove the noise. Hue Saturation Intensity (HSI) fusion algorithm was used to fuse the imaging spectroscopy and DSM as well as imaging spectroscopy and filtered intensity. The fusion of imaging spectroscopy and DSM was found to be better than that of imaging spectroscopy and LiDAR intensity quantitatively. The three datasets (imaging spectrocopy, DSM and Lidar intensity fused data) were classified into four classes: building, pavement, trees and grass using unsupervised classification and the accuracy of the classification assessed. The result of the study shows that fusion of imaging spectroscopy and LiDAR data improved the visual identification of surface features. Also, the classification accuracy improved from an overall accuracy of 84.6% for the imaging spectroscopy data to 90.2% for the DSM fused data. Similarly, the Kappa Coefficient increased from 0.71 to 0.82. on the other hand, classification of the fused LiDAR intensity and imaging spectroscopy data perform poorly quantitatively with overall accuracy of 27.8% and kappa coefficient of 0.0988. |
| first_indexed | 2025-11-15T08:58:37Z |
| format | Article |
| id | upm-28669 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:58:37Z |
| publishDate | 2013 |
| publisher | Science Publications |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-286692017-11-28T02:28:54Z http://psasir.upm.edu.my/id/eprint/28669/ Imaging spectroscopy and light detection and ranging data fusion for urban features extraction Idrees, Mohammed Mohd Shafri, Helmi Zulhaidi Saeidi, Vahideh This study presents our findings on the fusion of Imaging Spectroscopy (IS) and LiDAR data for urban feature extraction. We carried out necessary preprocessing of the hyperspectral image. Minimum Noise Fraction (MNF) transforms was used for ordering hyperspectral bands according to their noise. Thereafter, we employed Optimum Index Factor (OIF) to statistically select the three most appropriate bands combination from MNF result. The composite image was classified using unsupervised classification (k-mean algorithm) and the accuracy of the classification assessed. Digital Surface Model (DSM) and LiDAR intensity were generated from the LiDAR point cloud. The LiDAR intensity was filtered to remove the noise. Hue Saturation Intensity (HSI) fusion algorithm was used to fuse the imaging spectroscopy and DSM as well as imaging spectroscopy and filtered intensity. The fusion of imaging spectroscopy and DSM was found to be better than that of imaging spectroscopy and LiDAR intensity quantitatively. The three datasets (imaging spectrocopy, DSM and Lidar intensity fused data) were classified into four classes: building, pavement, trees and grass using unsupervised classification and the accuracy of the classification assessed. The result of the study shows that fusion of imaging spectroscopy and LiDAR data improved the visual identification of surface features. Also, the classification accuracy improved from an overall accuracy of 84.6% for the imaging spectroscopy data to 90.2% for the DSM fused data. Similarly, the Kappa Coefficient increased from 0.71 to 0.82. on the other hand, classification of the fused LiDAR intensity and imaging spectroscopy data perform poorly quantitatively with overall accuracy of 27.8% and kappa coefficient of 0.0988. Science Publications 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/28669/1/ajassp.2013.1575.1585.pdf Idrees, Mohammed and Mohd Shafri, Helmi Zulhaidi and Saeidi, Vahideh (2013) Imaging spectroscopy and light detection and ranging data fusion for urban features extraction. American Journal of Applied Sciences, 10 (12). pp. 1575-1585. ISSN 1546-9239; ESSN: 1554-3641 http://thescipub.com/abstract/10.3844/ajassp.2013.1575.1585 10.3844/ajassp.2013.1575.1585 |
| spellingShingle | Idrees, Mohammed Mohd Shafri, Helmi Zulhaidi Saeidi, Vahideh Imaging spectroscopy and light detection and ranging data fusion for urban features extraction |
| title | Imaging spectroscopy and light detection and ranging data fusion for urban features extraction |
| title_full | Imaging spectroscopy and light detection and ranging data fusion for urban features extraction |
| title_fullStr | Imaging spectroscopy and light detection and ranging data fusion for urban features extraction |
| title_full_unstemmed | Imaging spectroscopy and light detection and ranging data fusion for urban features extraction |
| title_short | Imaging spectroscopy and light detection and ranging data fusion for urban features extraction |
| title_sort | imaging spectroscopy and light detection and ranging data fusion for urban features extraction |
| url | http://psasir.upm.edu.my/id/eprint/28669/ http://psasir.upm.edu.my/id/eprint/28669/ http://psasir.upm.edu.my/id/eprint/28669/ http://psasir.upm.edu.my/id/eprint/28669/1/ajassp.2013.1575.1585.pdf |