SBIR-STTR Award

The United States Navy vision for Modeling and Simulation (M&S) includes making better analytical decisions, improving warfighting skills, and developing superior systems for maintaining the world's most powerful maritime forces for the joint force commanders. Over the past five years the potential for Light Detection and Ranging (LIDAR) data for the support Urban Warfare and various M&S initiatives has grown rapidly. LIDAR sensors capture the three-dimensional shape of the urban landscape at very high resolutions and accuracy. This resolution represents the character, or 3-D geometry, of the urban terrain, enabling precise line-of-sight, route analysis, cover and concealment, vulnerability assessment, and situational awareness to be performed. The data also provides the ability to extract objects from the scene such as individual buildings, trees, power lines, roads, and water. The Advanced LIDAR Exploitation System (ALES) program being conducted by the Joint Precision Strike Demonstration Project Office (JPSD-PO) will provide the M&S community with advanced database generation applications for the exploitation of LIDAR data. Visual Learning Systems, Inc. (VLS) was awarded the ALES contract by the JSPD-PO which will provide a significant advantage for implementing the Navy's Modeling and Simulation Master Plan

NAVAIR requires accurate and timely 3-D geospecific features, terrain, and imagery data to support Modeling and Simulation (M&S) software applications used in training and pre-mission rehearsal simulators. LIDAR data provides an excellent source for spatially accurate digital terrain and 3-D feature information; however, current visual database production techniques require hundreds of man-hours to extract and attribute features. Automated processes are necessary to achieve the Navy’s goal of good M&S data that is efficiently processed and correctly formatted. Bottlenecks in producing good data are a result of manual processes used in (1) registering old features and imagery to newly acquired imagery and LIDAR, (2) extracting and editing complex 3-D geometries, and (3) updating and attributing features. The proposed Phase II workplan promises to significantly alleviate these bottlenecks by developing a system that exploits the information content of LIDAR and multiband imagery using machine learning-based feature extraction technology. The envisioned system will have the capability of automating the extraction of M&S features from LIDAR, registering features to images, allowing dynamic interaction with analysts, interrogating analysts when it needs additional information, and embedding the ability to create a continuously updated repository of feature extraction models.

Keywords:
Automated Feature Extraction, Modeling And Simulation Systems, 3d Visualization, Lidar, Multispectral Color Imagery, Feature Analyst, Lidar Analyst, C