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Kevin GillJanuary 25, 2017

Need a break from Earth? Go stand on Mars with these lovely landscapes

We have two rovers on Mars: Curiosity and Opportunity, which are our only current means of acquiring on-the-ground imagery of the planet’s surface. As beautiful and awe-inspiring as these pictures are, they are limited to where the rovers go.

Take this panorama, for instance, imaged by the Opportunity rover back in 2010:

Intrepid Crater from Opportunity

NASA / JPL-Caltech / Cornell / ASU / Kevin M. Gill

Intrepid Crater from Opportunity
Intrepid Crater on Mars carries the name of the lunar module of the Apollo 12 mission. Opportunity recorded this view of the crater on sol 2417 (November 11, 2010). This view is presented in approximately true color, combining Pancam exposures from the 752, 535, and 432 nm filters.

Using images like this as both benchmarks and inspiration, I use real spacecraft data to approximate ground-level views of other places on Mars.

To do this, I work with two forms of data: Digital Terrain Models (DTMs) and satellite imagery. Simulating a camera on the ground requires very high resolution data, on the order of one or two meters per pixel. The best source for this data comes from HiRISE, the High Resolution Imaging Science Experiment, which is essentially a telescopic camera aboard the Mars Reconnaissance Orbiter. HiRISE is operated for NASA by the Lunar and Planetary Laboratory at the University of Arizona.

Here’s just one example of HiRISE data turned into a landscape:

Crater near Mawrth Vallis

NASA / JPL-Caltech / UA / Kevin M. Gill

Crater near Mawrth Vallis
A possible future landing site in southern Mawrth Vallis, created using HiRISE data. This is probably as close as I've gotten to the coloring I envisioned. Source data.

The HiRISE folks create DTMs using data from two or more passes of their camera over the same target. By taking into account the difference in viewing angles, and with the help of some serious computing power, the team generates three-dimensional models with very high accuracy and only a small amount of uncertainty.

The altimetry—essentially, the height component—is provided by the DTM, while the texture is provided by the images themselves, called orthoimagery. Unfortunately, the color swaths of each dataset are too narrow to use in landscape modeling, so only grayscale images from HiRISE’s context camera are used.

When looking for a region to model, I take the following into consideration:

1. Is the data of sufficiently high resolution?

2. Is the data of sufficiently high quality (lacking seams, obvious polygons, etc.)?

3. Are there geological features on which I can focus?

4. Is the data wide enough to provide a sufficient angle of view?

5. Does the data provide, or can I fake, a realistic horizon?

6. Will the resulting image be interesting?

The first two considerations make sure the image will look good without obvious pixilation or other artifacts, since I am attempting to make the model look as realistic as possible. If the resolution is too low, the model will look blocky or cartoony.

The other considerations make sure the image is worth working on in the first place. Datasets that are very long and narrow force me to use a very narrow angle of view from the camera and won’t capture enough scenery. In terms of features in the data, I find it difficult to make flat plains interesting, and random bumps are confusing. On the other hand, I love high ridges with rocky outcrops, and craters with ripples at the bottom. If the camera angle includes some sky, I need to create a distant horizon. When evaluating a dataset, I can determine if it provides a horizon itself or if I will be able to fake one. 

Sample Digital Terrain Model (DTM) and orthoimage

NASA / JPL-Caltech / UA / Kevin M. Gill

Sample Digital Terrain Model (DTM) and orthoimage
The DTM data defines the three-dimensional shape, whereas the orthoimagery defines what you see on the model.

Once I have selected a dataset to work with, I download the highest resolution DTM and imagery data available. The orthoimagery data is formatted as a JPEG2000 file with project-specific extensions, which means it won’t work in gdal, ImageMagick, and rarely in Photoshop without plugins. The easiest method to work with it is by downloading HiView, the HiRISE team’s custom JPEG2000 viewing tool, and using it to convert the image into a TIFF file. If the image resolution is too high, HiView won’t export at the full size. This is okay, since I need a resolution that I can easily divide into chunks less than 8192 pixels per side (a limitation of my graphics card when rendering) that won’t require too much memory when rendering.

I do the chunking of the HiView TIFF file with ImageMagick, using the convert+crop+repage command. The DTM comes in a IMG format which gdal can handle, so I run gdalinfo to get the data min/max, then convert it into an unsigned 16-bit GeoTIFF, scaling that min/max between 0 and 65,535. I run the ImageMagick convert command on that to split it up, but adding a depth option to maintain the 16-bit values. The 16-bit TIFF is important: If you only use 8 bit (default) then you’re binning into 255 possible values leading to a very terraced and fake-looking model, which I’ve noticed is a common mistake made by people modeling terrains.

Also, the number of tiles made from the DTM data need to match the number made from the orthoimagery; this is a reason why I need to split the input data into chunks smaller than 8192. Again, that’s the limit of what my renderer and graphics card will allow me to use. This would be quite limiting if it were not for UV tiling in Maya.

When I create the scene in Maya, all I’m really making is one long polygon plane mesh with dimensions that match that of the data. I generally use 128x128 polygon subdivisions. Then it’s really just a matter of setting up a material for the mesh. I render using Mental Ray in Maya, so I use mia_material_x_passes with the Matte Finish preset. I bind the orthoimagery to the diffuse input and the DTM data to the displacement input and set up the UV mapping. I run a test render and make sure it works. If it does, I can go and tweak the displacement scale, which I tend to exaggerate to bring out detail. 

Once a good camera angle is found, I’ll boost the mesh’s render subdivisions to 4, initial sample rate to 32, and extra sample rate to 24. 

Setting up the model in Autodesk Maya, a professional animation suite

Kevin M. Gill

Setting up the model in Autodesk Maya, a professional animation suite
Showing is the UV tiling scheme and a test render. Note how simple the model is: a single mesh and a light source.

At this point, the rendered image is only grayscale. Enter Photoshop. I have a Photoshop file I keep using that has a series of color adjustments inspired by images returned from Curiosity and Opportunity. A default sky is provided by a Curiosity MastCam image. A gradient overlay provides some atmospheric scattering, and tilt-shift blur adds focus effects. Some hue/saturation and brightness levels tweaks later, we have a final image.

Putting together the final image in Adobe Photoshop

Kevin M. Gill

Putting together the final image in Adobe Photoshop
A series of somewhat redundant filters provides me with the color I’m looking for.

Here are some more examples of the results. You can also view all of my images on Flickr.

Gullies in Gasa Crater

NASA / JPL-Caltech / UA / Kevin M. Gill

Gullies in Gasa Crater
This false color view of gullies in Gasa Crater was created using a digital terrain model (DTM) from the High Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter.
Gasa Crater, Mars

NASA / JPL-Caltech / UA / Kevin M. Gill

Gasa Crater, Mars
This false color view of gullies in Gasa Crater was created using a digital terrain model (DTM) from the High Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter.
Sinuous ridges in the Aeolis-Zephyria region, Mars

NASA / JPL-Caltech / UA / Kevin M. Gill

Sinuous ridges with scroll-bar deposits
Though you can't see the sinuous nature of the ridges, I loved this view for the ripples in the sand. Source data.
Fissure in the Cerberus region

NASA / JPL-Caltech / UA / Kevin M. Gill

Fissure in the Cerberus region
This false color view of a volcanic fissure in the Cerberus region of Mars was created using a digital terrain model (DTM) from the High Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter. The horizon was taken from Curiosity Mastcam imagery.
Bedrock exposure in landslide scarp

NASA / JPL-Caltech / UA / Kevin M. Gill

Bedrock exposure in landslide scarp
This false color view of bedrock exposures in a landslide scarp in Coprates Chasma—part of Valles Marineris—was created using a digital terrain model (DTM) from the High Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter.
Light-toned outcrops in Noctis Labyrinthus

NASA / JPL-Caltech / UA / Kevin M. Gill

Light-toned outcrops in Noctis Labyrinthus
This false color view of light-toned outcrops in Noctis Labyrinthus was created using a digital terrain model (DTM) from the High Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter.
North polar gypsum dunes in Olympia Undae

NASA / JPL-Caltech / UA / Kevin M. Gill

North polar gypsum dunes in Olympia Undae
This false color view was created using a digital terrain model (DTM) from the High Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter.
Inverted channels in Gale Crater

NASA / JPL-Caltech / UA / Kevin M. Gill

Inverted channels in Gale Crater
This false color view of a portion of Aeolis Mons ("Mount Sharp") within Gale Crater was created using a digital terrain model (DTM) from the High Resolution Imaging Science Experiment (HiRISE) camera aboard NASA's Mars Reconnaissance Orbiter.

Read more: pretty pictures, Opportunity, amateur image processing, Mars Exploration Rovers, Mars, 3D, Mars Reconnaissance Orbiter

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Kevin Gill
Kevin Gill

Software Engineer for Jet Propulsion Laboratory
Read more articles by Kevin Gill

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