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Maximizing urban features extraction from multi-sensor data with Dempster-Shafer theory and HSI data fusion techniques

This paper compares two multi-sensor data fusion techniques – Dempster-Sharfer Theory (DST) and Hue Saturation Intensity (HSI). The objective is to evaluate the effectiveness of the methods interm in space and time and quality of information extraction. LiDAR and hyperspectral data were fused using...

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التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Idrees, Mohammed Oludare, Saeidi, Vahideh, Pradhan, Biswajeet, Mohd Shafri, Helmi Zulhaidi
التنسيق: مقال
اللغة:English
منشور في: Asian Online Journals 2015
الوصول للمادة أونلاين:http://psasir.upm.edu.my/id/eprint/45421/1/LIDAR.pdf
http://psasir.upm.edu.my/id/eprint/45421/
https://ajouronline.com/index.php/AJAS/article/view/2320
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spelling my.upm.eprints.454212021-05-07T02:08:43Z http://psasir.upm.edu.my/id/eprint/45421/ Maximizing urban features extraction from multi-sensor data with Dempster-Shafer theory and HSI data fusion techniques Idrees, Mohammed Oludare Saeidi, Vahideh Pradhan, Biswajeet Mohd Shafri, Helmi Zulhaidi This paper compares two multi-sensor data fusion techniques – Dempster-Sharfer Theory (DST) and Hue Saturation Intensity (HSI). The objective is to evaluate the effectiveness of the methods interm in space and time and quality of information extraction. LiDAR and hyperspectral data were fused using the two methods to extract urban land scape features. First, digital surface model (DSM), LiDAR intensity and hyperspectral image were fused with HSI. Then the result was classified into five classes (metal roof building, non-metal roof building, tree, grass and road) using supervised classification (minimum distance) and the classification accuracy assessment was done. Second, Dempster Shafer Theory (DST) utilized the evidences available to fuse normalized DSM, LiDAR intensity and hyperspectral derivatives to classify the surface materials into five classes as before. It was found out that DST perform well in the ability to discriminate different classes without expert information from the scene. Overal accuracy of 87% achieved using DST. While in HSI technique, the overal accuracy obtained was 74.3%. Also, metal and non-metal roof types were clearly classified with DST which, does not have a good result with HSI. A fundamental setback of HSI is its limitation to fusion of only two sensor data at a time whereas we could integrate different sensor data with DST. Besides, the time required to select trainimg site for supervised classificition, the accuracy of feature classification with HSI fused data is dependent on the knowledge of the analyst about the scene with the other one. This study shows DST to be an accurate and fast method to extract urban features and roof types. It is hoped that the increasing number of remote sensing technology transforming to era of redundant data will make DST a desired technique available in most commercial image processing software packages. Asian Online Journals 2015-04 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/45421/1/LIDAR.pdf Idrees, Mohammed Oludare and Saeidi, Vahideh and Pradhan, Biswajeet and Mohd Shafri, Helmi Zulhaidi (2015) Maximizing urban features extraction from multi-sensor data with Dempster-Shafer theory and HSI data fusion techniques. Asian Journal of Applied Sciences, 3 (2). pp. 218-228. ISSN 2321-0893 https://ajouronline.com/index.php/AJAS/article/view/2320
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description This paper compares two multi-sensor data fusion techniques – Dempster-Sharfer Theory (DST) and Hue Saturation Intensity (HSI). The objective is to evaluate the effectiveness of the methods interm in space and time and quality of information extraction. LiDAR and hyperspectral data were fused using the two methods to extract urban land scape features. First, digital surface model (DSM), LiDAR intensity and hyperspectral image were fused with HSI. Then the result was classified into five classes (metal roof building, non-metal roof building, tree, grass and road) using supervised classification (minimum distance) and the classification accuracy assessment was done. Second, Dempster Shafer Theory (DST) utilized the evidences available to fuse normalized DSM, LiDAR intensity and hyperspectral derivatives to classify the surface materials into five classes as before. It was found out that DST perform well in the ability to discriminate different classes without expert information from the scene. Overal accuracy of 87% achieved using DST. While in HSI technique, the overal accuracy obtained was 74.3%. Also, metal and non-metal roof types were clearly classified with DST which, does not have a good result with HSI. A fundamental setback of HSI is its limitation to fusion of only two sensor data at a time whereas we could integrate different sensor data with DST. Besides, the time required to select trainimg site for supervised classificition, the accuracy of feature classification with HSI fused data is dependent on the knowledge of the analyst about the scene with the other one. This study shows DST to be an accurate and fast method to extract urban features and roof types. It is hoped that the increasing number of remote sensing technology transforming to era of redundant data will make DST a desired technique available in most commercial image processing software packages.
format Article
author Idrees, Mohammed Oludare
Saeidi, Vahideh
Pradhan, Biswajeet
Mohd Shafri, Helmi Zulhaidi
spellingShingle Idrees, Mohammed Oludare
Saeidi, Vahideh
Pradhan, Biswajeet
Mohd Shafri, Helmi Zulhaidi
Maximizing urban features extraction from multi-sensor data with Dempster-Shafer theory and HSI data fusion techniques
author_facet Idrees, Mohammed Oludare
Saeidi, Vahideh
Pradhan, Biswajeet
Mohd Shafri, Helmi Zulhaidi
author_sort Idrees, Mohammed Oludare
title Maximizing urban features extraction from multi-sensor data with Dempster-Shafer theory and HSI data fusion techniques
title_short Maximizing urban features extraction from multi-sensor data with Dempster-Shafer theory and HSI data fusion techniques
title_full Maximizing urban features extraction from multi-sensor data with Dempster-Shafer theory and HSI data fusion techniques
title_fullStr Maximizing urban features extraction from multi-sensor data with Dempster-Shafer theory and HSI data fusion techniques
title_full_unstemmed Maximizing urban features extraction from multi-sensor data with Dempster-Shafer theory and HSI data fusion techniques
title_sort maximizing urban features extraction from multi-sensor data with dempster-shafer theory and hsi data fusion techniques
publisher Asian Online Journals
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/45421/1/LIDAR.pdf
http://psasir.upm.edu.my/id/eprint/45421/
https://ajouronline.com/index.php/AJAS/article/view/2320
_version_ 1699238807596957696
score 13.251813

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