Since I last wrote, we’ve hired a new full-time employee. His name is Scott and we assigned him the task of learning ASP and LROC. The first utilities he’ll be contributing back to ASP are lronacjitreg and lronac2mosaic.py. The first utility is very similar to the ISIS utility with a similar name designed for HiRISE. The second utility, lronac2mosaic.py, uses the first tool and can take LRO-NAC EDR imagery and make a non-projected image mosaic. What lronac2mosaic.py does internally is ‘noproj’ and then ‘handmos’ the images together. There is an offset between the images due to model imperfections. Finding the correct offset so the combined images are seamless is the task of lronacjitreg. All of this just a streamed line version of what I wrote in a past blog post.
Previously users and our team only had the option to run all 4 combinations of the 4 LRO-NAC input files through ASP and then glue them together afterwards. Now with the use of lronac2mosaic, we can feed whole LRO-NAC observations into ASP and receive the full DTM in one go. No messy mosaicking of 4 files.
I’ve used Scott’s program successfully to recreate most DTMs that ASU has made via SOCET SET. Using my home server, I’ve been able to recreate 77 of their DTMs to date. We’ve been fixing bugs as we hit them. One of the biggest was in our search range guessing code. The next upcoming release of ASP will have the fruits of that labor. Previously ASP had a bad habit of ignoring elevation maximas in the image as it thought those IP measurements were noise. Now we should have a better track record of getting measurements for the entire image.
One of the major criticisms I’m expecting from the large dump of LRO-NAC DTMs we expect to deliver next year is what is the quality of the placement of our DTMs in comparison to LOLA. Another engineer we have on staff, Oleg, has just the solution for this. He has developed an iterative closest point (ICP) program called pc_align which will be in the next release. This is built on top of ETHZ Autonomous System Lab’s libpointmatcher and has the ability to take DTMs and align them to other DTMs or LIDAR data. This enables us to create well-aligned products that have height values agreeing within tens of meters with LOLA. Our rough testing shows us having a CE90 of 4 meters against LOLA after performing our corrections.
We’re not ready for the big production run yet. One problem still plaguing our process is that we can see the CCD boundaries in our output DTMs. We believe most of this problem is due to the fact that the angle between line of sight of the left and right CCDs changes with every observation. ISIS however only has one number programmed into it, the number provided by the FK. Scott is actively developing an automated system to determine this angle and to make a custom FK for every LRO-NAC observation. The second problem we’re tracking is that areas of high slope are missing from our DEMs. This is partially because we didn’t use Bayes EM for our test runs but it also seems like our disparity filtering is overly aggressive or just wrong. We’ll get on to that. That’s all for now!