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首页> 外文期刊>American journal of applied sciences >IMAGING SPECTROSCOPY AND LIGHT DETECTION AND RANGING DATA FUSION FOR URBAN FEATURES EXTRACTION | Science Publications
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IMAGING SPECTROSCOPY AND LIGHT DETECTION AND RANGING DATA FUSION FOR URBAN FEATURES EXTRACTION | Science Publications

机译：对城市特征提取的成像光谱和光检测和测距数据融合|科学出版物

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> 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.
机译：本研究提出了我们对城市特征提取的成像光谱（IS）和LIDAR数据的融合的研究结果。我们对高光谱图像进行了必要的预处理。最小噪声分数（MNF）变换用于根据其噪音排序高光谱带。此后，我们使用最佳指标因子（OIF）来统计地选择来自MNF结果的三个最合适的频带组合。使用无监督分类（K-Mean算法）分类合成图像，并评估分类的准确性。从LIDAR点云产生数字表面模型（DSM）和LIDAR强度。过滤激光雷达强度​​以消除噪音。 Hue饱和度强度（HSI）融合算法用于熔断成像光谱和DSM以及成像光谱和过滤强度。发现成像光谱和DSM的融合比定量成像光谱和激光雷达强度​​更好。三个数据集（成像光谱，DSM和LIDAR强度融合数据）分为四类：建筑，路面，树木和草，使用无人监督的分类和分类评估的分类准确性。该研究的结果表明，成像光谱和LIDAR数据的融合改善了表面特征的视觉识别。此外，分类精度从总精度从84.6％的总精度提高到DSM融合数据的成像光谱数据到90.2％。类似地，κ系数从0.71增加到0.82。另一方面，融合的激光雷达强度​​和成像光谱数据的分类定量，总精度为27.8％，kappa系数为0.0988。

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