TKO on Control Network Challenges31 May 2017
The saying, “work smarter not harder,” is exactly what ISIS programmers have in mind for users when challenging the capabilities and limits of tools and technologies while writing the next best app. Computer Scientist, Kris Becker, developed a feature-finding program called findfeatures with bells and whistles that builds a smarter network that cartographers can appreciate. Such networks build a foundation for controlled mosaics that requires precision and accuracy. Do you know that working with thousands of images at a time can slow production and consume a multitude of work hours if a foundation (control network) is weak? The new approach used in findfeatures encompasses a reduction of both human and computer resources, and we are excited because an even better findfeatures is forthcoming!
The goals of this new approach are to 1) significantly improve the image tie point measurement accuracy, 2) increase control point density, 3) reduce both human and computer resources required for producing and processing large control point networks, 4) efficiently identify and add new images to existing networks, and 5) create interpolated DEMs directly from bundle-adjusted control networks. Creating a DEM and a geometrically controlled basemap share common objectives, but creating a DEM from interpolation has one additional unique requirement: very high control point density.
Creation of the image control point network requires the most effort in the process of producing high quality controlled map products. For large datasets, the challenges are significant. Established techniques in ISIS3 are adequate when the dataset is small to modest, however as the dataset increases in size, productivity and quality are quickly diminished. Therefore, a reliable and efficient process must be applied to ensure a complete and high quality network.