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Proceedings Paper

LIDAR data compression using wavelets
Author(s): B. Pradhan; Shattri Mansor; Abdul Rahman Ramli; Abdul Rashid B. Mohamed Sharif; K. Sandeep
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Paper Abstract

The lifting scheme has been found to be a flexible method for constructing scalar wavelets with desirable properties. In this paper, it is extended to the LIDAR data compression. A newly developed data compression approach to approximate the LIDAR surface with a series of non-overlapping triangles has been presented. Generally a Triangulated Irregular Networks (TIN) are the most common form of digital surface model that consists of elevation values with x, y coordinates that make up triangles. But over the years the TIN data representation has become a case in point for many researchers due its large data size. Compression of TIN is needed for efficient management of large data and good surface visualization. This approach covers following steps: First, by using a Delaunay triangulation, an efficient algorithm is developed to generate TIN, which forms the terrain from an arbitrary set of data. A new interpolation wavelet filter for TIN has been applied in two steps, namely splitting and elevation. In the splitting step, a triangle has been divided into several sub-triangles and the elevation step has been used to 'modify' the point values (point coordinates for geometry) after the splitting. Then, this data set is compressed at the desired locations by using second generation wavelets. The quality of geographical surface representation after using proposed technique is compared with the original LIDAR data. The results show that this method can be used for significant reduction of data set.

Paper Details

Date Published: 28 October 2005
PDF: 12 pages
Proc. SPIE 5983, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V, 598305 (28 October 2005); doi: 10.1117/12.626579
Show Author Affiliations
B. Pradhan, Univ. Putra Malaysia (Malaysia)
Shattri Mansor, Univ. Putra Malaysia (Malaysia)
Abdul Rahman Ramli, Univ. Putra Malaysia (Malaysia)
Abdul Rashid B. Mohamed Sharif, Univ. Putra Malaysia (Malaysia)
K. Sandeep, Banaras Hindu Univ. (India)

Published in SPIE Proceedings Vol. 5983:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology V
Manfred Ehlers; Ulrich Michel, Editor(s)

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