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Urban Features Production with Combining LiDAR and Hyperspectral Data
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Seyed Yousef Sajjadi *   , Omid Aieneh |
| Tafresh University |
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Abstract: (3943 Views) |
| The main problems of hyper spectral data are large number of bands, high dependency between them and different signal to noise ratio in each band. To reduce dimensions of the feature space, minimizing noise and spectral dependence between bands, the MNF method has been applied to achieve better results in this paper. By applying this algorithm, the 144 bands of hyper spectral data were reduced to 19 suitable bands. Then from LiDAR data, the image height and intensity of the return signal received from the first and the last pulse of the laser were examined by LiDAR sensor. At last, the 19 spectral bands extracted from hyper spectral data have been fusion with 4 images of LiDAR data at the pixel level to create 23 suitable spectral bands. In order to detect and extract any study feature of the area on 23 spectral bands, seven different SVM methods were applied and finally by majority voting in the decision-making level between 7 obtained results, the class of each pixel was turned out. Morphology closing transform for repairing buildings and Hough transform for reconstructing the network effects of the fragmentation of land transportation were used on the results of pixels basis SVM method to regulate man-made side structure as well as the individual pixels which reduced. The results in this paper indicates the 99.52% overall accuracy and .958 kappa efficiency which compared to the GRSS chosen institution method. 0.6 Kappa coefficient has been improved. Used data are air-borne LiDAR and hyper spectral scenes requested and downloaded from the organized of a recent contest in data fusion domain. |
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| Keywords: Hyper Spectral, LiDAR, Morphology, Support Vector Machine, Fusion |
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Full-Text [PDF 1799 kb]
(1294 Downloads)
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Type of Study: Research |
Subject:
Aerial Photogrammetry
Received: 2017/11/1 | Accepted: 2018/01/10 | Published: 2018/06/21
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