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Classifier Fusion of Hyperspectral and Lidar Remote Sensing Data for Improvement of Land Cover Classifcation : Volume Xl-1/W3, Issue 1 (24/09/2013)

By Bigdeli, B.

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Book Id: WPLBN0004014539
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Classifier Fusion of Hyperspectral and Lidar Remote Sensing Data for Improvement of Land Cover Classifcation : Volume Xl-1/W3, Issue 1 (24/09/2013)  
Author: Bigdeli, B.
Volume: Vol. XL-1/W3, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Historic
Publication Date:
2013
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Samadzadegan, F., Reinartz, P., & Bigdeli, B. (2013). Classifier Fusion of Hyperspectral and Lidar Remote Sensing Data for Improvement of Land Cover Classifcation : Volume Xl-1/W3, Issue 1 (24/09/2013). Retrieved from http://community.worldlibrary.net/


Description
Description: Dept. of Photogrammetry, Faculty of Engineering, University of Tehran, North Kargar Street, Tehran, Iran. The interest in the joint use of remote sensing data from multiple sensors has been remarkably increased for classification applications. This is because a combined use is supposed to improve the results of classification tasks compared to single-data use. This paper addressed using of combination of hyperspectral and Light Detection And Ranging (LIDAR) data in classification field.

This paper presents a new method based on the definition of a Multiple Classifier System on Hyperspectral and LIDAR data. In the first step, the proposed method applied some feature extraction strategies on LIDAR data to produce more information in this data set. After that in second step, Support Vector Machine (SVM) applied as a supervised classification strategy on LIDAR data and hyperspectal data separately. In third and final step of proposed method, a classifier fusion method used to fuse the classification results on hypersepctral and LIDAR data. For comparative purposes, results of classifier fusion compared to the results of single SVM classifiers on Hyperspectral and LIDAR data. Finally, the results obtained by the proposed classifier fusion system approach leads to higher classification accuracies compared to the single classifiers on hyperspectral and LIDAR data.


Summary
CLASSIFIER FUSION OF HYPERSPECTRAL AND LIDAR REMOTE SENSING DATA FOR IMPROVEMENT OF LAND COVER CLASSIFCATION

 

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