The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-1/W5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 651–656, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-651-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 651–656, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-651-2015

  11 Dec 2015

11 Dec 2015

DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM

F. Samadzadega and H. Hasani F. Samadzadega and H. Hasani
  • School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Keywords: Hyperspectral, LiDAR, Fusion, SVM Classifier, Optimization, Parameter Determination, Feature Selection, Bees Algorithm

Abstract. Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.