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Proceedings Paper

Terrain classification
Author(s): Alok Sarwal; David Simon; Venkat Rajagopalan
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Paper Abstract

This work presents methods for terrain classification that support adaptive selection of parameters for Terrain Classification system. Work is also presented for water body detection and we present results from experiments conducted for water detection methods utilizing LADAR, color camera and polarization filter based sensors. Use of multiple sensors can provide better water detection capability. An approach for adaptive terrain classification is shown for existing rule-based classification algorithms. This approach allows us to develop a set of rules for various representative terrain types from various sites and operating conditions (light level, humidity, season, etc.) and exploit the onboard vehicle situational knowledge to select the most suitable set of rules for operation. An important element of this work requires use of data collected for different seasons and locations or terrain types in order to provide sensitivity measures. Existing terrain classification algorithms can utilize input from multiple sensors such as: Color, LADAR, FLIR and Multi-Spectral imagery. The performance of these algorithms is expected to improve as we acquire an increasing number of additional data sets that includes features of interest taken under various conditions of terrain-types types, illumination, temperature, humidity etc. and allow us to build a database of terrain knowledge. Environmental nformation and ground-truth is also collected along with the sensor data data. A Geographical Information System (GIS) interface is utilized along with related public-domain tools. Such tools are integrated to our system and used to provide data-management, spatial-modeling, and visualization.

Paper Details

Date Published: 30 September 2003
PDF: 8 pages
Proc. SPIE 5083, Unmanned Ground Vehicle Technology V, (30 September 2003); doi: 10.1117/12.487150
Show Author Affiliations
Alok Sarwal, PercepTek Inc. (United States)
David Simon, PercepTek Inc. (United States)
Venkat Rajagopalan, PercepTek Inc. (United States)


Published in SPIE Proceedings Vol. 5083:
Unmanned Ground Vehicle Technology V
Grant R. Gerhart; Charles M. Shoemaker; Douglas W. Gage, Editor(s)

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