Man on the Moon

During my internship at NASA in 2009, I helped produce an elevation model and image mosaic from Orbit 33 of Apollo 15. This mosaic was later burned into Google Earth’s Moon mode. Earlier this week it appears people have found an image of a man walking in the region of the Moon I stitched together. Here’s links to articles about this supposed extra terrestrial at The Nation,,, and Thank you to LROC’s Jeff Plescia for bringing this to my attention.

I quickly traced out that this section of the image mosaic comes from AS15-M-1151. This is a metric camera image from Apollo 15 that was scanned into digital form sometime in 2008 by ASU. What is shown in Google Earth is a reprojection of the image on to a DEM created by Ames Stereo Pipeline using said image. The whole strip of images was then mosaicked together using ASP’s geoblend utility. So this man could have been created by an error in ASP’s projection code. Below is the man in the moon from the raw unprojected form of the Apollo Metric image. Little man perfectly intact.

Unfortunately if you look at the next image in the film reel, AS15-M-1152, the man is gone. This is true also for 1153 and 1154. After that, the Apollo command module was no longer over looking the area. The metric camera takes a picture roughly every 30 seconds, so maybe the guy (who must be like 100 meters tall) just high tailed it.

These images come from film that had been in storage for 40 years. They were lightly dusted and then scanned. Unfortunately a lot of lint and hair still made it into the scans that we used for the mosaic. So much so, that Ara Nefian at IRG developed the Bayes EM correlator for ASP to work around those artifacts. Thus, this little Man in the image was very likely some hair or dust on the film. In fact if you search around the little man in image 1151 (in the top left corner of the image, just off an extension of a ray from the big crater) you’ll find a few more pieces of lint. Those lint pieces are also visible in Google Moon. However, it is still pretty awesome to find out others have developed a conspiracy theory on your own work. Hopefully it won’t turn into weird house calls like it did for friends of mine over the whole hidden nuclear base on Mars idea.

Update: You can find the Bad Astronomer’s own debunking of this man here. The cool bit is he tried to find the artifact in LRO and LO imagery. He then links to a forum where someone identifies that the dust was actually in the optics of the camera or in the scanner bed. So the man and other pieces of lint can be seen at roughly the same pixel location in consecutive frames.

Hah, it was even covered on SGU.

Man, I’m late to debunking my own work. :(

Advances in LRO-NAC processing in ASP

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 The first utility is very similar to the ISIS utility with a similar name designed for HiRISE. The second utility,, uses the first tool and can take LRO-NAC EDR imagery and make a non-projected image mosaic. What 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!

Second Thought on LRO-NAC NoProj

Mark Rosiek from USGS Astrogeology expressed some doubt in my noproj recipe for LRO-NAC. This is completely reasonable because if everything were perfect, there would be no offset after the noproj step between the LE and RE CCDs. He requested 2 DEM samples of Tsiolkovsky Crater so he could compare to USGS work. I decided I’d try the same during free time through out the day. Unforunately I couldn’t find a trusted reference DEM to compare against. I can’t find ASU’s result of this area on their RDR webpage and it is impossible to use LMMP Portal. Can you even download the actual elevation values from LMMP? No I don’t want your color render or greyscale! 

Next best idea is to just difference the two DEMs I created and compare them. I didn’t bundle adjust these cameras at all so their placement to each other is off by some ~150 meters. After ICPing them together and then differencing them I can produce an error map. The gradient or shape of the error can help clue into how bad my fiddling in the ideal camera was. Below is the result between the stereo pairs M143778723-M143785506 and M167363261- M167370048.

You can definitely see the CCD boundary in this error map. You can see the CCD boundary in the hillshade. It’s about a 1 meter jump when looking at a single DEM. Error map seems to agree with this and shows a peak error at 4 meters in the CCD boundary location.

So what is the cause of these errors? Well we have two sources, (1) the projection into the ideal camera model and (2) bad spacecraft ephemeris. I’d argue that most of the difference between these two DEMs is the spacecraft ephemeris. That’s top priority in my book to correct. However when I look at the disparity map for these stereo pairs, there is a definitive jump at the CCD boundary. The cause of that would be an improper model of the angle between LE and RE cameras in ISIS. This should be expected. They’re currently not modeling the change in camera angles with respect to temperature. Also when looking at the vertical disparity, it seems one side is always brighter than the other. This suggests that I didn’t quite have the correct values for input to handmos.

Trying to fix all of this now might be a mistake on my part. I know that ASU will eventually produce new SPICE data that contains corrections from LOLA and a USGS study. I also imagine that eventually the work of E. J. Speyerer et al. will be implemented in ISIS.

Creating a registered LRO-NAC DEM without GCPs

I’ve previously written a tutorial on performing a bundle adjustment, jigsaw in ISIS lingo, using CTX. Let’s try something a little bit more complex with LRO-NAC. If you are not previously aware, LRO-NAC is actually two separate optical assemblies that are joined by the spacecraft bus. Ideally this spacecraft hardware would be solid as a rock and wouldn’t bend. Unfortunately that’s not the case, so what would have been a bundle adjustment between 2 cameras is now 4 in the case of LRO-NAC.

LRO-NAC Data Preparation

Picking a stereo pair has always been the most difficult part of the job for me. I honestly don’t know how to find stereo pairs. So instead I’m just going to recreate something ASU did in SOCET SET. If you are not aware, ASU has processed about 100 DEMs with LRO-NAC. I’m going to recreate their Moore F Crater DEM using M125713813 and M125720601. You can download the raw data via PDS.

I picked this stereo pair blindly and it ended up being extremely difficult to register correctly. In order to help out, I added a fifth image to the bundle adjustment to give another orbit’s worth of spacecraft ephemeris into the measurements. That image was M110383422LE and I picked it because it overlapped 2 of my previous images and had the same lighting conditions as my previous images.

Once you have downloaded all the images, prep the files in ISIS with the following:

parallel "lronac2isis from={} to={.}.cub; lronaccal from={.}.cub to={.}.cal.cub; spiceinit from={.}.cal.cub; rm {.}.cub" ::: *IMG

* The ‘parallel’ command is GNU Parallel, a non-standard system utility that I love dearly.

Attaching a custom Elevation Source

From a previous article I wrote about, we saw that map projecting against the WAC Global DTM gave much better results than working against the sparse LOLA that is the default for map projecting by in ISIS. So I’m going to do that now. Once we have a successful jigsaw result, then we’ll map-project. If you forgot how to attach the WAC Global DTM, here are the commands I used. My image pair was at 37.3N and 185E. So the tile WAC_GLD100_E300N2250_100M.IMG from this link was what I needed.

pds2isis from=WAC_GLD100_E300N2250_100M.IMG to=WAC_GLD100_E300N2250_100M.cub
algebra from=WAC_GLD100_E300N2250_100M.cub to=WAC_GLD100_E300N2250_100M.lvl1.cub operator=unary A=1 C=1737400
demprep from=WAC_GLD100_E300N2250_100M.lvl1.cub to=WAC_GLD100_E300N2250_100M.lvl2.cub
rm WAC_GLD100_E300N2250_100M.cub WAC_GLD100_E300N2250_100M.lvl1.cub

parallel spiceinit from={} shape=USER model=$PATH_TO/WAC_GLD100_E300N2250_100M.lvl2.cub ::: *cal.cub

Making the control network

Making the control network is pretty straightforward and is just time consuming. I performed the commands I previously outlined here on this blog.


Group = PolygonSeederAlgorithm
      Name = Grid
      MinimumThickness = 0.01
      MinimumArea = 1
      XSpacing = 600
      YSpacing = 1000


Object = AutoRegistration
   Group = Algorithm
     Name         = MaximumCorrelation
     Tolerance    = 0.7

   Group = PatternChip
     Samples = 19
     Lines   = 19
     MinimumZScore = 1.5
     ValidPercent = 80

   Group = SearchChip
     Samples = 120
     Lines   = 100


parallel footprintinit from={} ::: *cal.cub
echo *.cub | xargs -n1 echo > cube.lis
findimageoverlaps from=cube.lis overlaplist=overlap.lis
autoseed fromlist=cube.lis overlaplist=overlap.lis deffile=autoseed.def networkid=lronac pointid=???? description=moon
pointreg fromlist=cube.lis deffile=autoRegTemplate.def
qnet (editting and fixing

I had to spend a lot of time in Qnet for this stereo pair. About half of my automatic matches didn’t work. (Possibly my search range was too small?) In order to correct this, I had to then manually edit the output control network in Qnet. I then filter down all the control points to the ones marked ‘Ignored’. Then I just manually align the measurements. You just need to get the measures close to the right spot by clicking on the same feature. Afterwards, you can then click the ‘Register’ button to get subpixel accurate alignment.

Autoseed and AutoReg also have a bad habit of only matching LE images to LE, and RE images to RE. It is a good idea to run along the edge of an LE image and try to find common points that are seen in both RE images. These are going to be the few control points that have more than 2 control measures. I also had to do this between my 5th image and the other 2 images that it had overlap with. This problem was likely because the overlap between these 3 images didn’t meet the requirement of MinimumThickness in Autoseed.def.

Normally at this point I would then recommend to you to start picking out ground control points. I tried that. Then I went a little insane. I’m better now. Instead we’re going to do crazy stuff in the next section.

The reason we’re not using GCPs, is that the only source to align against is the WAC Image mosaic. I tried this and I could easily get a bundle adjustment result that aligned well with the image mosaic. However the output DEM would always show a massive error between my height results and the results of LOLA and the WAC Global DTM. After adding the 5th image and clamping the trust of my GCP to 50 meters in radius, I got a result that matched LOLA and WAC DTM in an okay manner but didn’t really align at all to the WAC image mosaic. Things made sense once I compared the image mosaic to a hillshade of WAC DTM.

WAC’s Image mosaic doesn’t agree with WAC’s Global DTM! Ghaaaa! Madness! I was so incredibly disappointed. I didn’t know what to do at this point, so I just dropped the ground control points.

Bundle Adjusting

Alrighty then, time to start bundle adjusting with jigsaw. Likely on the first run things are going to blow up because bad measurements still exist in the control network. My solution to this was to first have jigsaw solve for only a few camera parameters and then check out the output control network for features that had the highest error. In Qnet you can filter control measures that have errors higher than a certain amount of pixels. After I fixed those measurements manually, I resave the control network as my input network and then redo the jigsaw.

Each time I redo jigsaw, I let jigsaw solve for more parameters. My pattern was:

jigsaw fromlist=cube.lis radius=no twist=no cnet= update=no
jigsaw fromlist=cube.lis radius=no twist=yes cnet= observations=no update=no
jigsaw fromlist=cube.lis radius=yes twist=yes cnet= observations=no update=no
jigsaw fromlist=cube.lis radius=yes twist=yes cnet= observations=no update=no camsolve=velocities

After iterating many times, I eventually produced a control network and jigsaw result that had a sigma0 of .34 pixels. However, if we’re to write this camera solution to file and then make a DEM, we would see that I have worse alignment with LOLA than if we hadn’t bundle adjusted at all.

So how can we get a good bundle adjustment result that aligns well with LOLA? One idea would be to pick GCPs directly against LOLA using their gridded data product. I know folks in the USGS who have done exactly this. However I don’t even see how they can determine correct alignment, so I’m going to ‘nix that idea. Another idea would be to have Jigsaw not solve for radius (radius=no), but instead use only the radius values provided by LOLA. This will keep problem in the ball park, but the resulting jigsaw solution will have a sigma0 around 15 pixels! Not solving for radius essentially ties our hands behind our back in the case of LRO-NAC when our radius source is so incredibly low res.

What we need instead is to simply constrain / rubber-band our Control Points to have radius values that are similar to LOLA. Unfortunately, how do we determine what correct Lat Long to sample the LOLA radius for each control measure? My solution was to not define that. I did two things: (1) I changed all my control measures from ‘free’ to ‘constrained’ with a sigma of 200,200,50. This can be performed with cneteditor. (2) I then wrote and ran the following bash script.

#/bin/env bash

cp $input_control

for i in {0..100}; do
    echo Iteration $i
    cnetnewradii cnet= onet= model= $radius_source getlatlon= adjusted
    jigsaw fromlist=cube.lis radius=yes twist=yes cnet=  onet= update=yes spsolve= position camsolve= velocities


Your mind should be blown at this point. I’ve essentially written an iterative closest point algorithm using the ISIS utilities jigsaw and cnetnewradii. This process is only able to work because we have a perfect control network that we vetted with the previous jigsaw runs. This also works because our LOLA data here is not flat, and has plenty of texture. Finally, LRO-NAC was close to the correct solution. Imagine images with horrible attitude data, like Apollo, would just blow up with this technique.

On each iteration, the sigma0 shouldn’t change. Or if it changes, it should be extremely small. That’s because a translation or scale change of the control points and camera location has no effect on the reprojection error. What should be reducing on each iteration is the standard deviation of the radius correction for each control point as listed in bundleout.txt. The sparse 3D model of the problem in Jigsaw is slowly fitting itself to the LOLA DEM but constrained only by the camera geometry. The standard deviation of the radius correction will never go to zero and will eventually plateau. This is because there will always be some error against LOLA since large portions of the measurements are interpolations between orbits.

Disclaimer: I’ve only tried this technique for this LRO-NAC stereo pair. I don’t know if it will work for others. However this was the only technique I could come up with that would produce results that best aligned with LOLA. I couldn’t get anything good to come out of using WAC Image Mosaic GCPs.

Map projecting for Stereo

Now that we have an updated ephemeris, we’re ready to perform a map projection to speed up stereo. Be sure to not accidentally spiceinit your files, which would wipe your updated ephemeris. Notice that I’m map projecting everything at a low resolution. This is because I’m in a hurry for this demo. In a production environment for LRO-NAC, we would likely map project at 1 MPP or 0.5 MPP. This step and ASP’s triangulation are usually the longest part of this entire process.

parallel cam2map pixres=mpp resolution=5 from={} to={/.}.map.cub ::: ../*cal.cub

Running ASP

Nothing special here, I’m just processing the LE-LE, RE-RE, and LE-RE combination. RE-LE stereo pair wouldn’t resolve anything at 5 m/px. Afterwards, I gdalbuildvrt the outputs together. The massive lines separating the individual DEMs that make up this stereo pair may concern you. However that effect will diminish if I had map projected my inputs at a higher resolution. The reason being that, correlation is much like convolution, in that we erode the input image by a half kernel size along the image border. That border gets perceivably smaller if the image is of higher resolution.

parallel -n 2 stereo M125713813{1} M125720601{2} {1}{2}/{1}{2} ::: LE LE RE RE LE RE
parallel -n2 point2dem -r moon --t_srs \"+proj=eqc +lat_ts=37 +lat_0=0 +lon_0=180 +x_0=0 +y_0=0 +a=1737400 +b=1737400 +units=m +no_defs\" --tr 5 --orthoimage {1}{2}/{1}{2}-L.tif {1}{2}/{1}{2}-PC.tif --nodata -32767 ::: LE LE RE RE LE RE
gdalbuildvrt aspba_drg.vrt */*-DRG.tif
gdalbuildvrt aspba_dem.vrt */*-DEM.tif
parallel gdal_translate -co TILED=yes -co COMPRESS=LZW -of GTiff {} {.}.tif ::: *.vrt

Conclusion and Comparison

Now the moment you’ve been waiting for. The point where I have to prove I’m not crazy. I have three versions of this stereo pair. There is the result produced by ASU using SOCET SET and then there’s ASP with and without Bundle Adjustment. The look of ASP’s result will be a little shoddy because I map projected my input at 5 m/px which prevent me from get the most detail out. It did however save me a lot processing time while I experimented this method out.

Here are the hillshades for those 3 datasets overlaid a hillshade of the LRO-WAC Global DTM which is 100 m/px. There’s not much to tell, but you can at least see that everyone seems to have decent horizontal alignment.

Here are the difference maps between those 3 datasets and the LRO-WAC Global DTM.

Here are the difference maps between those 3 datasets and the LOLA Gridded Data Product, which I resampled to 100 m/px. I think this shows straight forward the improvement bundle adjustment made to the output DEM.

Just for giggles, I also did a difference between the ASU DEM and the bundle adjusted ASP DEM. They’re pretty close to each other. There seems to just be a simple tilt between the two or that the ASP DEM is just shifted to a slightly lower latitude. Who’s more correct is anyone’s guess for now.


Ideally at this point I would then compare the ASU DEM and the BA’d ASP DEM against the raw LOLA shot points. This is where curiosity got me and where I move on to the next personal project. Maybe someone else would be interested in a more rigorous review.

Also, I’m not sure if the 5th image is required.

Making well registered DEMs with ISIS and Ames Stereo Pipeline

Measurements of the Satellite’s position and pose are often not perfect. Those minor mistakes can cause gross offsets when producing DEMs from cameras with such long focal lengths. Correcting this is achieved with bundle adjustment. I’ve discussed this before, but I’d like to expand my previous article by adding ground control points.

Ground Control points are special measurements in an image that have well known geodetic position. On earth, this might be achieved with a large target (an expedition’s tent) that can be observed with a satellite and has a GPS beacon. We don’t currently have such an analog on the Moon or Mars. The best bet is to instead compare an image against a good world map and then pick out individual features. For Mars, I think the best map to register against is the THEMIS global Day IR 100 m map. For the Moon, I would pick the 100 m LRO-WAC DTM and Image mosaic.

In the following sections, I’m going to run through an example for how to register a pair of stereo CTX observations to the THEMIS global map. For those who are interested, my example images are of Hrad Vallis and use P17_007738_2162_XN_36N218W and P18_008028_2149_XN_34N218W.

Getting a THEMIS Global Map Tile into ISIS

The 11th version of the THEMIS Day IR map is available from the following ASU link:

Unfortunately the data you download is just a simple PGM file that mentions nothing about how it was georeferenced. That information can only be obtained through careful reading from the website. You however just need to know that the data is in Simple Cylindrical projection, it’s Geocentric, it’s sampled at 592.75 PPD, and that the files are indexed by their lower left corner.

I’m going to leaving it up to you to read the documentation to see what the following commands in ISIS do. That documentation is available here from USGS’s website. The tile I’m processing is “lat30_lon120.pgm” and I choose it specifically because it contains my example CTX images. I had to use an intermediate GDAL command because std2isis couldn’t read a pgm file.

gdal_translate -of GTiff lat30_lon120.pgm lat30_lon120.tif
std2isis from=lat30_lon120.tif to=lat30_lon120.cub
maptemplate map=map.template projection=SIMPLECYLINDRICAL resopt=ppd resolution=592.75 clon=0 targetname=Mars targopt=user lattype=planetocentric eqradius=3396190.0
maplab sample=0 line=0 coordinates=latlon lat=60 lon=120 from=lat30_lon120.cub map=map.template

Creating Image to Image Measurements

I’ve discussed this process before, and I’m not going to re-describe it. (I’m quite lazy). However, the following is a quick recap of the commands I used to do it.


Group = PolygonSeederAlgorithm
      Name = Grid
      MinimumThickness = 0.01
      MinimumArea = 1
      XSpacing = 4000
      YSpacing = 4000


Object = AutoRegistration
   Group = Algorithm
     Name         = MaximumCorrelation
     Tolerance    = 0.7

   Group = PatternChip
     Samples = 19
     Lines   = 19
     MinimumZScore = 1.5
     ValidPercent = 80

   Group = SearchChip
     Samples = 75
     Lines   = 75

The ISIS Commands:

parallel spiceinit from={} ::: *cub
parallel footprintinit from={} ::: *cub
echo *cub | xargs -n1 echo > cube.lis
findimageoverlaps from=cube.lis overlaplist=overlap.lis
autoseed fromlist=cube.lis overlaplist=overlap.lis deffile=autoseed.def networkid=ctx pointid=???? description=mars
pointreg fromlist=cube.lis deffile=autoRegTemplate.def

Then you’ll need to do clean up in ISIS’s Qnet after you’ve completed the above commands. In my experience, pointreg will only correlate maybe 75% of your control measures correctly. To clean these up quickly, just go into qnet and select that you want to filter your control points to show only those that have been “ignored”. Then go and save. I had an excess of control points, and decided to just delete a lot of my ignore points. You can see a distribution of my control points in the following Qnet screenshot.

Creating Ground Control Measurements

Finally, we are breaking new ground here. We are going to measure some ground control points against the THEMIS map tile we downloaded earlier. You could do this in Qnet, however in practice I find this segfaults a lot (I’m using ISIS 3.3.1). So instead we are going to do this process with Qtie.

Open Qtie by simply just typing “qtie” into your terminal. Then click open button or command “O”. It will ask you for a basemap and you should select your THEMIS tile that is in cube format. It will then ask you for a “cube to tie to base”, select your first CTX image of the stereo pair. When it asks you for a control network, click cancel. If you actually give it your pointreg’d control network, it will go into an infinite loop because that control network has no GCPs.

You’ll then want to use the ‘tie’ tool to create a bunch of GCP. If you don’t remember, you have to right click on your CTX image to create a new measurement. You’ll want to capture at least 3 GCPs if you are working with a frame camera. Do about 10 evenly distributed GCPs if you have a linescan camera. CTX is a linescan camera, so have fun clicking.

When matching a low-resolution image to a high-resolution image, I find it very helpful to turn on the affine alignment option in the “Tie Point Tool”. That is the “Geom” radio button on the right side of the window. I also find it very helpful to have the left window constantly flipping between the two input images at a high rate. You can achieve this by clicking the play button in the lower left and then lowering the number next to the play button. You’ll also want to zoom in on your basemap.

On the left is an example of a match I found. This process is difficult and takes some patience. You’ll want to look at your match from different scales just to make sure that you haven’t aligned to some pareidolia.

Once you finish, you oddly have to click the diskette in the “Tie Point Tool” to save your control network. I’ve saved mine as “”. If you were selecting ground control points against the LRO-WAC Global Image mosaic (for registering images of the moon), you might want to take a detour here and use the “cnetnewradii” command. That way you can have your GCPs use radius measurements from the LRO-WAC Global DTM. Otherwise your radius measurements will come from ISIS’s default sources of an interpolated LOLA or MOLA for Mars.

We now want to merge our GCP control network to our control network we created with pointreg. This is done with the following:

cnetmerge inputtype=cnets networkid=CTX_with_gcp description="against themis 100m"

Back in qnet, you’ll want to tie the other CTX image to your new GCPs. You can do this by open each GCP point up by double clicking it, and then selecting “Add Measure(s) to Point” in the Qnet Tool window. A screenshot is shown left. You’ll then need to manually align the control measures. Be sure not to move your reference control measure from the first CTX image. You’ll also want to change your GCP’s PointType from “Fixed” to “Constrained”. This will allow your bundle adjustment session to move your GCP to reduce its reprojection error. However it will be rubber banded to the measurement you made in Qtie. This is helpful because your basemap source will usually have mistakes.

After having registered all your GCPs to the other image, and also having set their type to “constrained”, you’ll want to do one last thing. Select all your ground control points in the Control Network Navigator and then press the “Set Apriori/Sigmas” button. I have a screenshot below. The sigmas are your uncertainty measurement for your GCP and basically tell bundle adjustment just how much it can ignore or trust your GCP measurements. Since our measurements were all made from the same source, we’re using the same sigma for all our GCPs. In my opinion a good sigma value is two times the pixel resolution of your basemap. So in this example, I’m putting the GCPs as being accurate to 200 meters in longitude, latitude, and radius directions.

Bundle Adjusting

Alright, it’s time to bundle adjust. This almost exactly the same command we ran before, however we can turn on a new option we couldn’t before:

jigsaw fromlist=cube.lis radius=yes twist=no spsolve=position

This time we can solve for the spacecraft’s position since we have GCPs! Do a test run without updating the spice. If it converges to a sigma0 less than 2 px, then you can go ahead an add the “update=yes” option. If you are working with LRO-NAC images, you can likely converge to less than 1 px sigma0. If you unfortunately didn’t converge or your sigma0 has a high value, you likely have a messed up measurement in your control network. This probably came from pointreg, where it managed to match to the incorrect repeated texture. You can identify the mistaken control point by looking at the output residuals.csv file that was created by jigsaw. Just go an manually align the control points in Qnet that show a high residual error. The error should be obvious in Qnet. If it is not, then likely jigsaw fitted to the outliers. If that happened, the control points with low residual errors will have the obvious misregistration in Qnet.

My final DEM results

Creating the above measurements and performing jigsaw took me about 2 hours. Creating the DEMs in Ames Stereo Pipeline took another 3 hours. Here are my results with and without my jigsaw solution rendered in Google Earth. I’ve overlayed a hillshaded render of my DEMs at 50% opacity on top of the THEMIS Map I registered to. My DEMs also look a little crummy since I just used parabola subpixel for speed in processing.

Bundle Adjusted DEM

Non Bundle Adjusted DEM

Finished 3D from Apollo

Render of a DIM and DEM map from Apollo Metric Images It’s been a long 3 years in the making, but today I can finally say that I have finished my 3D reconstruction from the Apollo Metric cameras. After ten of thousands of CPU hours and several hundreds of liters soda, the Mapmakers at the Intelligent Robotics Group have managed to produce an Image mosaic and Digital Elevation Map. The final data products are going up on LMMP’s website for scientists to use. I encourage everyone else to instead take a look at the following KML link I’ve provided below.

IRG’s Apollo Metric/Mapping Mosaic

It’s so pretty! But don’t be sad! IRG’s adventure with Apollo images doesn’t end here. Next year we’ll be working on a new and fancier Image Mosaic called an Albedo Map. Immediately after that, our group will be working with the folks at USGS’s Astrogeology Science Center to include more images into the Apollo mosaic. In that project we’ll include the images that are not only looking straight down on the Moon, but also the images that look off into the horizon.

All of the above work was produced using our open source libraries Vision Workbench and Ames Stereo Pipeline. Check them out if you find yourself producing 3D models of terrain. At the very least, our open source license allows you to look under the hood and see how we did things so that you may improve upon them!