Application of image classification techniques to multispectral lidar point cloud data

被引：14

作者：

Miller, Chad I. ^{[1
,2
]}

Thomas, Judson J. ^{[1
,2
]}

Kim, Angela M. ^{[1
,2
]}

Metcalf, Jeremy P. ^{[1
,2
]}

Olsen, Richard C. ^{[1
,2
]}

机构：

[1] SAIC, 1710 SAIC Dr, Mclean, VA 22102 USA

[2] Naval Postgrad Sch, 833 Dyer Rd, Monterey, CA 93943 USA

来源：

LASER RADAR TECHNOLOGY AND APPLICATIONS XXI | 2016年 / 9832卷

关键词：

lidar; image classification; multispectral lidar; point cloud;

D O I：

10.1117/12.2223257

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Data from Optech Titan are analyzed here for purposes of terrain classification, adding the spectral data component to the lidar point cloud analysis. Nearest-neighbor sorting techniques are used to create the merged point cloud from the three channels. The merged point cloud is analyzed using spectral analysis techniques that allow for the exploitation of color, derived spectral products (pseudo-NDVI), as well as lidar features such as height values, and return number. Standard spectral image classification techniques are used to train a classifier, and analysis was done with a Maximum Likelihood supervised classification. Terrain classification results show an overall accuracy improvement of 10% and a kappa coefficient increase of 0.07 over a raster-based approach.

引用

页数：12