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30 September 2003 Terrain classification
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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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alok Sarwal, David Simon, and Venkat Rajagopalan "Terrain classification", Proc. SPIE 5083, Unmanned Ground Vehicle Technology V, (30 September 2003);

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