Tree Classification with Fused Mobile Laser Scanning and Hyperspectral Data

Sensors

Eetu Puttonen¹  Anttoni Jaakkola²  Paula Litkey² 

[1] Department of Photogrammetry and Remote Sensing, Finnish Geodetic Institute, P.O. Box 15 02431 Masala, Finland;

关键词: mobile laser scanning;    classification;    data fusion;    hyperspectrum;    forestry;   

DOI  :  10.3390/s110505158

来源: mdpi

【 摘 要 】

Mobile Laser Scanning data were collected simultaneously with hyperspectral data using the Finnish Geodetic Institute Sensei system. The data were tested for tree species classification. The test area was an urban garden in the City of Espoo, Finland. Point clouds representing 168 individual tree specimens of 23 tree species were determined manually. The classification of the trees was done using first only the spatial data from point clouds, then with only the spectral data obtained with a spectrometer, and finally with the combined spatial and hyperspectral data from both sensors. Two classification tests were performed: the separation of coniferous and deciduous trees, and the identification of individual tree species. All determined tree specimens were used in distinguishing coniferous and deciduous trees. A subset of 133 trees and 10 tree species was used in the tree species classification. The best classification results for the fused data were 95.8% for the separation of the coniferous and deciduous classes. The best overall tree species classification succeeded with 83.5% accuracy for the best tested fused data feature combination. The respective results for paired structural features derived from the laser point cloud were 90.5% for the separation of the coniferous and deciduous classes and 65.4% for the species classification. Classification accuracies with paired hyperspectral reflectance value data were 90.5% for the separation of coniferous and deciduous classes and 62.4% for different species. The results are among the first of their kind and they show that mobile collected fused data outperformed single-sensor data in both classification tests and by a significant margin.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.