The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is an established, first-principles based scene simulation tool that produces synthetic multi-spectral and hyperspectral images from the visible to long wave infrared (0.4 to 20 microns). Over the last few years, significant enhancements such as spectral polarimetric and active Light Detection and Ranging (lidar) models have also been incorporated into the software, providing an extremely powerful tool for algorithm testing and sensor evaluation. However, the extensive time required to create large-scale scenes has limited DIRSIG's ability to generate scenes "on demand." To date, scene generation has been a laborious, time-intensive process, as the terrain model, CAD objects and background maps have to be created and attributed manually. To shorten the time required for this process, we have developed a comprehensive workflow aimed at reducing the man-in-the-loop requirements for many aspects of synthetic hyperspectral scene construction. Through a fusion of 3D lidar data with passive imagery, we have been able to partially-automate many of the required tasks in the creation of high-resolution urban DIRSIG scenes. This paper presents a description of these techniques.
Registration of maps and airborne or satellite images is an important problem for tasks such as map updating and change
detection. This is a difficult problem because map features such as roads and buildings may be mis-located and features
extracted from images may not correspond to map features. Nonetheless, it is possible to obtain a general global
registration of maps and images by applying statistical techniques to map and image features. Finer analysis can then be
used to find changes and local mismatches. The Maximization of Mutual Information (MMI) technique has proven to be
very robust in image-to-image registration. This paper extends the MMI technique to the map-to-image registration
problem through a focus-of-attention mechanism that forces MMI to utilize correspondences that have a high probability
of being information rich. The number of registration parameters can be adjusted to meet the characteristics of the
matching problem and accuracy requirements of the application. Experimental results demonstrate the robustness and
efficiency of the algorithm.