On Aug 6, 2022, at 12:21 PM, mikita belikau <miki...@gmail.com> wrote:
I have a question: how to georeference CTX images so that when you open several CTX overlapping images there are no gaps between them? Key point is to remove gaps (shifts).
--
You received this message because you are subscribed to the Google Groups "Ames Stereo Pipeline Support" group.
To unsubscribe from this group and stop receiving emails from it, send an email to ames-stereo-pipeline...@googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/b1ddcef7-5e78-4e15-91a1-cdc08497ce25n%40googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/7caf1222-ef1e-4df8-8887-ed009dc684f1n%40googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/249c8722-3ac1-4457-b33e-f5f08b17b89en%40googlegroups.com.
Dear Colleagues
I wanted to support what Divya had said (thanks, Divya!) about the importance of HRSC and provide further information.
All HRSC DTM (Digital Terrain Model) products which have v50 and above [1,2] or are within a controlled photomosaic are co-aligned to MOLA [3]. The HRSC DTM products (single-strip) are available via https://pds-geosciences.wustl.edu/missions/mars_express/hrsc.htm but their actual source location is https://archives.esac.esa.int/psa/ftp/MARS-EXPRESS/HRSC/MEX-M-HRSC-5-REFDR-DTM-V1.0/
The photocontrolled mosaics described in [3,4] are available from http://hrscteam.dlr.de/HMC30/. They have been in production in 2015. There are 5 half-quadrangles in the public domain. You can visualise each HRSC single-strip DTM and the HRSC controlled photomosaic DTMs and ORIs at http://i-mars.eu/web-gis.php.
A single 50m DTM and 12.5m ORI HRSC mosaic (not in a map-quad format) of the whole of Vallis Marineris is available at https://www.cosmos.esa.int/web/psa/UCL-MSSL_Valles-Marineris_HRSC_v1.0 and the paper describing its production at [5]. There is also a HRSC mosaic of the South Pole available at https://www.cosmos.esa.int/web/psa/UCL-MSSL_iMars_HRSC_v1.0 and described in [6].
There is a multi-resolution (HRSC, CTX and HiRISE) DTM and ORI mosaic of the Oxia Planum (Rosalind Franklin ExoMars rover) site available at https://www.cosmos.esa.int/web/psa/ucl-mssl_oxia-planum_hrsc_ctx_hirise_madnet_V1.0 and described in [7] along with a super-resolution 1m colour ORI and 2m DTM using SINGLE IMAGES only using the MADNet 2.0 (called "Super-3D") at https://www.cosmos.esa.int/web/psa/ucl-mssl_oxia-planum_cassis_srr-madnet_v1.0 and described in [8].
There are ≈3500 CTX DTM products most of which are co-aligned to HRSC DTMs and ORIs produced using CASP-GO based on ASP (https://github.com/mssl-imaging/CASP-GO) available at https://www.cosmos.esa.int/web/psa/UCL-MSSL_iMars_CTX_v1.0 which are described in [9]. All the UCL products are available through https://www.cosmos.esa.int/web/psa/ucl-mssl_meta-gsf. There will be some huge lunar MADNet products coming soon so please watch this space.
We
are working on a 3m DTM of the whole of Valles Marineris using
Super-3D based on CTX images co-registered and
orthorectified to the aforementioned 12.5m HRSC ORI
mosaic by Sebastian Walter at FU Berlin.
We
also developed an automated co-registration and ortho
rectification scheme (ACRO, [10-12]) which was applied to
thousands of CTX scenes of the South Pole [6] as well as to
making a CTX mosaic of the MC11 map-quad reported in [12,13].
This software (part of which is available at
https://github.com/UCL-iMars) is unsupported and in MatLab but
the rest could be made available if someone is prepared to
invest the time. Please get in touch if that is of interest.
It should be noted that the global CTX product available from Caltech and the global THEMIS product are NOT orthorectified and therefore are not co-aligned to anything apart form the 463m MOLA DTM which at the equator is really only 2km due to the sparse coverage.
Do get in touch if you have any follow-up questions
Best regards
Jan-Peter
-- Professor Jan-Peter Muller Head, Imaging Group Mullard Space Science Laboratory Department of Space and Climate Physics University College London Holmbury St. Mary Dorking Surrey RH5 6NT, UK http://maps.google.co.uk/maps?f=q&hl=en&q=rh5+6nt&z=17&t=h Tel:+44-1483-204151 Fax:+44-1483-278312 email: j.mu...@ucl.ac.uk https://www.ucl.ac.uk/mssl/people/prof-jan-peter-muller
[5] Tao, Y.; Michael, G.; Muller, J.-P.; Conway, S. J.; Putri, A. R. D. Seamless 3D Image Mapping and Mosaicing of Valles Marineris on Mars Using Orbital HRSC Stereo and Panchromatic Images. Remote Sensing 2021, 13, 1385. doi:10.3390/rs13071385
[6] Putri, A. R. D., Sidiropoulos, P., Muller, J. P., Walter, S. H., & Michael, G. G. (2019). A New South Polar Digital Terrain Model of Mars from the High Resolution Stereo Camera (HRSC) onboard the ESA Mars Express. Planetary and Space Science. DOI: 10.1016/j.pss.2019.02.010
[7] Tao, Y.; Xiong, S.; Conway, S. J.; Muller, J.-P.; Guimpier, A.; Fawdon, P.; Thomas, N.; Cremonese, G. Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets. Remote Sens. 2021, 13, 2877. doi:10.3390/rs13152877
[8] Tao, Y., Xiong, S.; Muller, J.-P.; Michael, G.; Conway, S. J.; Paar, G.; Cremonese, G.; Thomas, N. Subpixel-Scale Topography Retrieval of Mars Using Single-Image DTM Estimation and Super-Resolution Restoration. Remote Sensing 2022, 14, 1–25.doi:10.3390/rs14020257
[9] Tao, Y., Muller, J. P., Sidiropoulos, P., Xiong, S.-T., Putri, A. R. D., Walter, S. H. G., Veitch-Michaelis, J., Yershov, V. (2018) Massive Stereo-based DTM Production for Mars on Cloud Computers. Planetary Space Science, 154, 30–58. DOI: 10.1016/j.pss.2018.02.01.
[10]
Sidiropoulos, P. &
Muller, J.-P. A Systematic Solution to Multi-Instrument
Coregistration of High-Resolution Planetary Images to an
Orthorectified Baseline. Ieee T Geosci Remote 56, 78–92 (2018).
[11] Sidiropoulos, P., Muller, J.-P., Watson, G., Michael, G. & Walter, S. Automatic Coregistration and orthorectification (ACRO) and subsequent mosaicing of NASA high-resolution imagery over the Mars MC11 quadrangle, using HRSC as a baseline. Planetary Space Science 151, 33–42 (2017).
[13] Sidiropoulos, P. & Muller, J.-P. Large-Scale Co-Registration of Mars High-Resolution NASA Images to HRSC: A Case-Study of the MC11-E Quadrangle. in vol. 47 2034 (Lunar and Planetary Science Conference, 2016).
--
You received this message because you are subscribed to a topic in the Google Groups "Ames Stereo Pipeline Support" group.
To unsubscribe from this topic, visit https://groups.google.com/d/topic/ames-stereo-pipeline-support/G9kRgwFeM_I/unsubscribe.
To unsubscribe from this group and all its topics, send an email to ames-stereo-pipeline...@googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/8bf92650-b1e3-ad37-0d5a-96f25c650198%40ucl.ac.uk.
pc_align /Original/HMC_13E20_dt5.tif /Original/h2228_0002_bl3.lbl --max-displacement 10000 -o aligned
which gave me following output:
ASP 3.0.1-alpha
Build ID: 23a8216c
Build date: 2022-05-17
pc_align /Original/HMC_13E20_dt5.tif /Original/h2228_0002_bl3.lbl --max-displacement 10000 -o aligned
uname -a
Darwin Laptops-MBP.fritz.box 21.5.0 Darwin Kernel Version 21.5.0: Tue Apr 26 21:08:22 PDT 2022; root:xnu-8020.121.3~4/RELEASE_X86_64 x86_64
sysctl -a hw 2>/dev/null | grep -E "ncpu|byteorder|memsize|cpufamily|cachesize|mmx|sse|machine|model" | grep -v ipv6
hw.ncpu: 4
hw.byteorder: 1234
hw.memsize: 17179869184
hw.perflevel0.l1icachesize: 32768
hw.perflevel0.l1dcachesize: 32768
hw.perflevel0.l2cachesize: 262144
hw.perflevel0.l3cachesize: 4194304
hw.optional.mmx: 1
hw.optional.sse: 1
hw.optional.sse2: 1
hw.optional.sse3: 1
hw.optional.supplementalsse3: 1
hw.optional.sse4_1: 1
hw.optional.sse4_2: 1
hw.cpufamily: 1479463068
hw.cachesize: 17179869184 32768 262144 4194304 0 0 0 0 0 0
hw.l1icachesize: 32768
hw.l1dcachesize: 32768
hw.l2cachesize: 262144
hw.l3cachesize: 4194304
Vision Workbench log started at 2022-08-11 22:13:01.
2022-08-11 22:13:01 {0} [ console ] : Detected datum from /Original/HMC_13E20_dt5.tif:
Geodetic Datum --> Name: D_MARS Spheroid: MARS Semi-major axis: 3396000 Semi-minor axis: 3396000 Meridian: Reference_Meridian at 0 Proj4 Str: +proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +a=3396000 +b=3396000 +units=m +no_defs
2022-08-11 22:13:01 {0} [ console ] : Will use datum (for CSV files): Geodetic Datum --> Name: D_MARS Spheroid: MARS Semi-major axis: 3396000 Semi-minor axis: 3396000 Meridian: Reference_Meridian at 0 Proj4 Str: +proj=eqc +lat_ts=0 +lat_0=0 +lon_0=0 +x_0=0 +y_0=0 +a=3396000 +b=3396000 +units=m +no_defs
2022-08-11 22:13:01 {0} [ console ] : Computing the intersection of the bounding boxes of the reference and source points using 9000000 sample points.
2022-08-11 22:15:02 {0} [ console ] : Reference box: (Origin: (78.5086, 14.7424) width: 11.7319 height: 15.5158)
2022-08-11 22:15:02 {0} [ console ] : Source box: (Origin: (77.054, 7.38124) width: 4.0071 height: 13.6041)
2022-08-11 22:15:02 {0} [ console ] : Intersection reference box: (Origin: (78.5086, 14.7424) width: 2.55241 height: 6.24288)
2022-08-11 22:15:02 {0} [ console ] : Intersection source box: (Origin: (78.5086, 14.7424) width: 2.55241 height: 6.24288)
2022-08-11 22:15:02 {0} [ console ] : Intersection of bounding boxes took 120.684 [s]
2022-08-11 22:15:02 {0} [ console ] : Reading: /Original/HMC_13E20_dt5.tif
2022-08-11 22:15:12 {0} [ console ] : Loaded points: 19447395
2022-08-11 22:15:12 {0} [ console ] : Loading the reference point cloud took 9.95805 [s]
2022-08-11 22:15:12 {0} [ console ] : Reading: /Original/h2228_0002_bl3.lbl
2022-08-11 22:15:14 {0} [ console ] : Loaded points: 4606303
2022-08-11 22:15:14 {0} [ console ] : Loading the source point cloud took 2.18368 [s]
2022-08-11 22:15:14 {0} [ console ] : Data shifted internally by subtracting: Vector3(565037,3.1738e+06,1.04707e+06)
2022-08-11 22:15:14 {0} [ console ] : Loading reference as DEM.
2022-08-11 22:15:14 {0} [ console ] : Building the reference cloud tree.
2022-08-11 22:15:38 {0} [ console ] : Reference point cloud processing took 23.8119 [s]
2022-08-11 22:15:39 {0} [ console ] : Filtering gross outliers
2022-08-11 22:17:06 {0} [ console ] : Filtering gross outliers took 87.8384 [s]
2022-08-11 22:17:06 {0} [ console ] : Reducing number of source points to 100000
2022-08-11 22:17:10 {0} [ console ] : Number of errors: 100000
2022-08-11 22:17:10 {0} [ console ] : Input: error percentile of smallest errors (meters): 16%: 4636.03, 50%: 4965.27, 84%: 5128.57
2022-08-11 22:17:10 {0} [ console ] : Input: mean of smallest errors (meters): 25%: 4451.96, 50%: 4670.75, 75%: 4790.27, 100%: 5034.47
2022-08-11 22:17:10 {0} [ console ] : Initial error computation took 4.05211 [s]
2022-08-11 22:22:09 {0} [ console ] : Match ratio: 0.75001
2022-08-11 22:22:09 {0} [ console ] : Alignment took 298.412 [s]
2022-08-11 22:22:09 {0} [ console ] : Number of errors: 100000
2022-08-11 22:22:09 {0} [ console ] : Output: error percentile of smallest errors (meters): 16%: 34.9832, 50%: 110.51, 84%: 354.852
2022-08-11 22:22:09 {0} [ console ] : Output: mean of smallest errors (meters): 25%: 27.3362, 50%: 54.6555, 75%: 87.1502, 100%: 351.565
2022-08-11 22:22:09 {0} [ console ] : Final error computation took 0.508349 [s]
2022-08-11 22:22:09 {0} [ console ] : Alignment transform (origin is planet center):
0.9981307946889654 0.01480199133395067 -0.05929433148375537 3785.381563750911
-0.01413245186957091 0.999831732678318 0.01169530391323897 -5667.984098547138
0.0594574679725776 -0.01083546870321696 0.9981720303235636 -1110.772715296247
0 0 0 1
2022-08-11 22:22:09 {0} [ console ] : 2022-08-11 22:22:09 {0} [ console ] : Centroid of source points (Cartesian, meters): Vector3(577775.16,3179670.8,1038291.4)
2022-08-11 22:22:09 {0} [ console ] : Centroid of source points (lat,lon,z): Vector3(17.811151,79.701203,-1566.3641)
2022-08-11 22:22:09 {0} [ console ] :
2022-08-11 22:22:09 {0} [ console ] : Translation vector (Cartesian, meters): Vector3(-11793.935,-2225.2647,-3108.9133)
2022-08-11 22:22:09 {0} [ console ] : Translation vector (North-East-Down, meters): Vector3(-1645.2428,11206.084,5042.9061)
2022-08-11 22:22:09 {0} [ console ] : Translation vector magnitude (meters): 12398.147
2022-08-11 22:22:09 {0} [ console ] : Maximum displacement of points between the source cloud with any initial transform applied to it and the source cloud after alignment to the reference: 23210.212 m
2022-08-11 22:22:09 {0} [ console ] : Warning: The input --max-displacement value is smaller than the final observed displacement. It may be advised to increase the former and rerun the tool.
2022-08-11 22:22:09 {0} [ console ] : Translation vector (lat,lon,z): Vector3(-0.02791236,0.19893747,-5023.9819)
2022-08-11 22:22:09 {0} [ console ] :
2022-08-11 22:22:09 {0} [ console ] : Transform scale - 1 = -4.773959e-15
2022-08-11 22:22:09 {0} [ console ] : Euler angles (degrees): Vector3(-0.62193913,-3.4086724,-0.81119203)
2022-08-11 22:22:09 {0} [ console ] : Euler angles (North-East-Down, degrees): Vector3(0.27078703,0.018475645,3.5524998)
2022-08-11 22:22:09 {0} [ console ] : Axis of rotation and angle (degrees): Vector3(0.18128273,0.95547769,0.2328067) 356.43719
2022-08-11 22:22:09 {0} [ console ] : Writing: aligned-transform.txt
2022-08-11 22:22:09 {0} [ console ] : Writing: aligned-inverse-transform.txt
2022-08-11 22:22:09 {0} [ console ] : Writing: aligned-beg_errors.csv
2022-08-11 22:22:10 {0} [ console ] : Writing: aligned-end_errors.csv
2022-08-11 22:22:11 {0} [ console ] : Writing: aligned-iterationInfo.csv
2022-08-11 22:22:11 {0} [ console ] : Saving to disk took 1.361244 [s]
From the output it seems to be Ok'ish, but I'd like to see this aligned HRSC file which I don't see. Should I run some additional command
What are my next steps (assuming my high-level plan is correct)? Should I run bundle_adjust or mapproject?
I am asking, cause documentation contains sample of usage for another data (like csv) and it's not clear what to do next.
> pc_align /Original/HMC_13E20_dt5.tif /Original/h2228_0002_bl3.lbl --max-displacement 10000 -o aligned
I don't quite know what the .lbl thing is. The tool expects DEMs in .tif files. I never used it with .lbl.
> 2022-08-11 22:22:09 {0} [ console ] : Translation vector magnitude (meters): 12398.147
That is a huge translation, 12 km. I am not sure what you are doing here. I don't know how one can get two DEMs on Mars to be so far off.
> From the output it seems to be Ok'ish, but I'd like to see this aligned HRSC file which I don't see. Should I run some additional command
See the pc_align doc for how to save the output point clouds.
> What are my next steps (assuming my high-level plan is correct)? Should I run bundle_adjust or mapproject?
> I am asking, cause documentation contains sample of usage for another data (like csv) and it's not clear what to do next.
You need to create stereo DEMs. That is a lot of work. You can start here: https://stereopipeline.readthedocs.io/en/latest/tutorial.html
You received this message because you are subscribed to the Google Groups "Ames Stereo Pipeline Support" group.
To unsubscribe from this group and stop receiving emails from it, send an email to ames-stereo-pipeline...@googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/d3cc8359-1aa9-4c38-abe2-06f2fe47746bn%40googlegroups.com.
Dear Colleagues
I addressed the specific comment about the Murray labs product but perhaps this was missed "It should be noted that the global CTX product available from Caltech and the global THEMIS product are NOT orthorectified and therefore are not co-aligned to anything apart from the 463m MOLA DTM which at the equator is really only 2km due to the sparse coverage of MOLA footprints."
There are an array of CTX and HiRISE products available for Jezero crater which USGS and JPL have produced, see https://astrogeology.usgs.gov/maps/mars-2020-jezero-crater-landing-site-controlled-orthomosaics which includes 3 CTX and 6 HiRISE products.
Best
Peter
⚠ Caution: External sender
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/CANkZX87zMW%3DukCJt%3DBZibkZvX4KttBAXO70430ziZpyHa1GMFg%40mail.gmail.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/7b895f1c-8db9-4d22-b300-dbbea01e23b3n%40googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/7f227ab4-bd22-48d5-9285-2e2856d5291cn%40googlegroups.com.
Dear Mikita
It looks like the CTX products were coaligned vertically with MOLA (https://astrogeology.usgs.gov/search/map/Mars/Mars2020/Jez_MARS2020_CTX_DEM_Science_Mosaic) and some horizontal georeferencing was done in ArcGIS Pro but against what image base is not stated. I thought I had seen that this was against HRSC but I cannot find any reference for this in any of these official products.
A HRSC map half-quad has been created of HRSC which has 50m DTM and 12.5m ORI with the PAN band available at the previously referenced official HRSC site at http://hrscteam.dlr.de/HMC30/MC13E/
I would start by looking at the offset of the CTX-ORI to the HRSC-ORI and then HiRISE wrt CTX. Hopefully it wont be spatially variant.
Good luck
Jan-Peter
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/8af282a1-bba3-4f93-b7fe-fa6376f042a8n%40googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/8af282a1-bba3-4f93-b7fe-fa6376f042a8n%40googlegroups.com.
Dear Mikita
The standard method for the UoA/HiRISE-PDS is the use of MOLA withoin the SOCET system developed by USGS to provide a base albeit the resolution difference is huge (≈1600 x). Another method is to mosaic individual HiRISE images together and then orthorectify to DTMs like MOLA.
Our approach is to create a cascade of spatial resolutions starting with HRSC level-4 products (with version #≥50) available from the ESA-PSA or mirrored PDS or if you're lucky the HRSC level-5 mosaiced images which have been employed bundle block adjustment (see Gwinner et al., 2016 for a full description of this standard photogrammetric technique). These HRSC products include DTMs at 50-150m spacing (dependent on the original *.ND (panchromatic) and *.S1 & *.S2 off-nadir stereo views (which are usually half the GSD of the nadir.
Using these HRSC ORI single-strip or mosaiced products (typically
100 images per half-quadrangle) you can then apply the ISIS
interest-operator based automated co-registration (ACR) operator
with your CTX images, create the stereo-based DTM and orthorectify
the image. You can then repeat this exercise with HiRISE images
using the CTX (6m) ORI image strips (or mosaic) as the new base
image for HiRISE.
Hope that helps
Best regards
Peter
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/7a9d155c-2e86-4273-ada0-0ced3fb4f713n%40googlegroups.com.
Dear Mikita
I have put inline responses below.
Good luck
Best regards
Peter
Dear Jan-Peter!
Thanks a lot for your message.
So, just to confirm that got it right:
1) Since every team uses it's own pipeline/approach for creating the ORI HiRISEs (and possibly DTMs for them) it's Ok that ORIs from different sources are not aligned.
And it's not just the pipeline, but also an auxiliary data that is used for this.
As I understand, your team uses CASP-GO software which has several modules and not all the modules are publicly available. So, e.g. enthusiasts like me can not use this approach. Is this correct?
2) Considering that CASP-GO is not fully available publicly how do you think what other pipelines (like Chicago method, AU, etc) I can use for my initial task - to have globally aligned HiRISE (and possibly CTX/HRSC)? Do you have experience with any of them
3) Another question for me is where to take stereo pairs images for generating DTMs. As I understand regardless of what pipeline/method you stick to you anyway need stereo pairs. For HiRISE images I can take them here https://www.uahirise.org/stereo/ , but it's not that clear where to take them e.g. for CTX. ODE interface https://ode.rsl.wustl.edu/mars/productsearch
gives all the images that satisfy certain criteria, but how to filter out only stereo pairs it's not clear. Maybe it does not have this at all. So, my question is where to take images for stereo pairs e.g. for CTX?
That is a GIS question and best targetted at a GIS analyst. I understand that the ODE shapefiles can be manipulated in ARCgis to extract possible stereo-pairs from any instrument which is not designed to capture stereo, like CTX or HiRISE. I have no familiarity with these tools and relied on former members of my group or collaborators at JPL and the Université de Nantes to do this for us. We do have MatLab code [1] which can find stereo and/or repeat and this was partially re-written by a former PhD student who has moved back to the Far East Asia.
The MatLab algorithm (non-GIS) is described in
Stereo criteria for LROC-NAC which is also generally applicable
to HiRISE and CTX is described in
[2] Henriksen, M. R. et
al. Extracting accurate and precise topography from LROC
narrow angle camera stereo observations. Icarus 283,
122–137 (2017).
There was an old stereo-CTX catalogue produced by Ross Beyer in 2012 and we updated this to June 2016. It is downloadable from http://i-mars.eu/imars-products.php
Something that bitter experience showed us, you MUST look at the
images before you try to match them as there are bad images due to
situations such as transmission data drop-outs and dust storms. We
tried to automate this filtering process [3] to obviate having to
look at RDR images but we never managed to develop a robust system
for CTX or HiRISE images due to lack of staff resources.
To view this discussion on the web visit https://groups.google.com/d/msgid/ames-stereo-pipeline-support/e64e16db-1327-4355-83f8-729e8b1a12cen%40googlegroups.com.
Is there a chance somehow to take part in testing of this NASA PDART C(ASP)-GO? I don't see any reference for this publicly.