GeoVisipedia (Geospatial Visual Wikipedia) is a new and novel approach to sharing knowledge about complex geospatial entities such as facilities. Facilities are composed of interconnected objects such as buildings, chemical processing units, electrical generation equipment and similar structures. Satellite imagery of a facility reveals a great deal about the organization and visual appearance of objects in a facility, but very little about the identity or function of the object. For example, given a satellite imagery of an oil refinery, an expert in refining readily identifies distillation units and can explain how they work. A non-expert would have a very difficult time identifying these objects let alone explaining how they function.
To make highly complex information accessible to non-experts, GeoVisipedia associates a wiki page with objects in satellite imagery. A user selects an object in the image and a wiki page appears that provides the user with detailed information about the object. Experts can author information into the wiki and this information is shared with other users. Additionally, GeoVisipedia automatically transfers all wiki pages from one image of a facility to other imagery of the facility. Consequentially, knowledge about objects in the facility integrates over time as new imagery becomes available and as new wiki pages are created and additional information is added to existing wiki pages. In this respect, satellite imagery becomes a portal to expert knowledge and insight about objects in a facility.
Gradient direction matching (GDM) is the main target identification algorithm used in the Image Content Engine project at Lawrence Livermore National Laboratory. GDM is a 3D solid model-based edge-matching algorithm which does not require explicit edge extraction from the source image. The GDM algorithm is presented, identifying areas where performance enhancement seems possible. Improving the process of producing model gradient directions from the solid model by assigning different weights to different parts of the model is an extension tested in the current study. Given a simple geometric model, we attempt to determine, without obvious semantic clues, if different weight values produce significantly better matching accuracy, and how those weights should be assigned to produce the best matching accuracy. Two simple candidate strategies for assigning weights are proposed: pixel-weighted and edge-weighted. We adjust the weights of the components in a simple model of a tractor/semi-trailer using relevance feedback to produce an optimal set of weights for this model and a particular test image. The optimal weights are then compared with pixel and edge-weighting strategies to determine which is most suitable and under what circumstances.
The Image Content Engine (ICE) is being developed to provide cueing assistance to human image analysts faced with increasingly large and intractable amounts of image data. The ICE architecture includes user configurable feature extraction pipelines which produce intermediate feature vector and match surface files which can then be accessed by interactive relational queries. Application of the feature extraction algorithms to large collections of images may be extremely time consuming and is launched as a batch job on a Linux cluster. The query interface accesses only the intermediate files and returns candidate hits nearly instantaneously. Queries may be posed for individual objects or collections. The query interface prompts the user for feedback, and applies relevance feedback algorithms to revise the feature vector weighting and focus on relevant search results. Examples of feature extraction and both model-based and search-by-example queries are presented.