NDLI: Improved Classification of Building Infrastructure from Airborne Lidar Data Using Spin Images and Fusion with Ground-Based Lidar

Content Provider	IEEE Xplore Digital Library
Author	Caceres, J.J. Slatton, K.C.
Copyright Year	2007
Description	Author affiliation: Univ. of Florida, Gainesville (Caceres, J.J.; Slatton, K.C.)
Abstract	Over the last five to ten years, Airborne Laser Swath Mapping (ALSM) technology, also known simply as Lidar, has become widely available to the remote sensing research community. In that time, many fields of application have been proposed. The majority of the early successful applications were for terrain mapping and surveying, but the high resolution data delivered by this technology has allowed researchers to apply it to problems in forestry, civil engineering, urban planning, and many other fields. In urban planning for example, explicit three-dimensional (3D) building models are an important data product for estimating microwave line-of-sight communications and emergency responder plans. Such models can be extracted from the 3D ALSM point cloud data once the points are properly segmented into building and non-building classes. The work presented here focuses on improved segmentation of building points in the ALSM point cloud using a local subspace mapping known as a spin image. The ALSM data are then fused with ground-based laser scanner data to improve discriminability among complex building architectures, adjacent vegetation, and even rooftop infrastructure.
Starting Page	1
Ending Page	7
File Size	410261
Page Count	7
File Format	PDF
ISBN	1424407117
DOI	10.1109/URS.2007.371862
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-11
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Laser radar Clouds Buildings Urban planning Terrain mapping Forestry Microwave communication Remote sensing Civil engineering
Content Type	Text
Resource Type	Article

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