Here are 67 RGB images from orbits 1700 – 1800. It’s a diverse collection of incredible images, making it difficult to pick a favorite. As always, click anywhere in the image to launch the JP2 and zoom in.

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PSP_001764_1880 (Zunil Crater rim) stands out to me, since I used it early on when putting together our color processing pipeline. It looked absolutely grey, so I figured I had made a mistake. Then I saw that gorgeous swath of blue on the crater rim, where it looks like a small landslide has exposed fresher material, and I knew everything was starting to work properly.

But a short list of the must-see-RGB would have to include these:

• PSP_001784_2630
• PSP_001784_2030
• PSP_001756_1995
• PSP_001754_2020 and
• PSP_001752_1750.

There are two nice isolated gullies: PSP_001712_1405 & PSP_001714_2390.

PSP_001720_1730 is missing one-half of the RGB color, due I think to IR channels that weren’t received. A recent update to our color processing will allow to go ahead and automatically produce the RGB product in cases like this.

The transition between dunes and an extremely steep scarp in PSP_001728_1995 is quite striking (see below, zoomed out 4x).

How about the Boulder race in PSP_001730_1740?

PSP_001732_2595 shows an interesting type of patterned ground, where boulders have shifted into a regular series of repeating lines.

PSP_001782_1195 is giving me trouble, some browsers won’t display it here; it is a bin-4 37500-line image.

Today, our software group provided a set of major updates to our downlink operations team. It was the first major update in many months. One of the most anticipated features is smarter “stretch” algorithm for our color products (RDR Extras). As discussed in a previous post, a stretch (in image processing terms), is a mapping between one range of pixel values and another. In our case, it provides our viewers with a better-looking image up-front, with less need to adjust parameters in display software such as IAS (though this is still often very helpful when zoomed in). As always, the full range of original data is preserved in the RDR JP2.

Our former algorithm for the NOMAP and Quicklook products said that the pixel values above the brightest 0.1% and below the darkest 0.1% would be mapped to the extreme values, with a linear fit in between. For a majority of images, this was a good choice that showed excellent contrast but prevented too much saturation.

However, 0.1% (a thousandth) of a two Gigapixel image is still two million pixels. So if there were a particularly bright spot, like a rocky outcrop amid a field of dunes, or a particularly dark spot, like a cavern opening in a plain of boulders, then all the saturation would occur in that one area, washing it out completely, and lowering contrast everywhere else in the image. So the algorithm needed to be more adaptable. After a good deal of experimentation, the algorithm we settled on looks at the brightest and darkest pixels in a thumbnail version of the image, and uses those values for the extremes, instead of the values at 0.1%. We shrink a copy of each color band to 1/11th the original scale. Pixel values in the original below the darkest in the thumbnail are mapped to pure black, while pixel values above the highest are mapped to pure white. The stretched bands are then merged to make the color image. Hence, a bright or dark spot smaller than 1/11th x 1/11th of the image size will no longer dominate the stretch.

What this ultimately means is, our RDR Extras now show more detail in areas that would be completely washed out by the old algorithm.

For example, in this ‘cave’ image, the left is from the original RGB.NOMAP.JP2, while the right is the same product using the new algorithm. As you can see, previously you could not tell if there was a floor to the hole or if it sloped away to greater depths.

The new algorithm is used strictly for the JP2’s; the browse and thumb are already scaled down enough that it would not make a substantial difference with them. The new algorithm went into effect today; coincidentally we just started orbit 8000. Images with the new stretch will likely appear in upcoming weekly releases and we plan to reprocess everything with this change (and improved calibration) during the summer.

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Here are 64 observations from the 1600 block of PSP street. Additionally, I have updated my three previous posts with images I missed the first time around.

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PSP_001656_2175 is perhaps the most striking of the group, with prominent slope streaks. Slope streaks are also visible in PSP_001644_1715.

My personal favorites are the dune images: PSP_001608_2560 and PSP_001660_2570.

Two images have the “glow” problem: PSP_001662_1195 and PSP_001697_2570.

Here are 40 RGB color images from the 1500 – 1600 orbit range of MRO.

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There are, as always, many magnificent images here. Some of the noteworthy observations are:

PSP_001521_2025 and PSP_001501_2280: On the HiRISE web site you can see diagrams made by Tim Parker show the locations of various parts (lander, backshell, heatshield or parachute) for Viking Lander 1 and Viking Lander 2. It’s possible they aren’t in the color strip (I haven’t found them)!

PSP_001508_1245 and PSP_001510_2195: These two exhibit a “glow” pattern of saturated pixels due to high TDI (Time Delay Integration) settings on the blue-green CCDs. (All of the exposure settings are chosen for each observation based on a photometric model of the scene).

PSP_001538_2035: This is a rim-to-rim section across a crater called Tooting that is about 30 kilometers in diameter. It’s also interesting to note how the altitude of the rims, when combined with the large off-nadir roll angle (23 degrees), leads to an oddly bowed geometric projection. But it is correct; as the terrain rose, fell, and rose again from HiRISE’s angled point of view, the center of the ground track deviated slightly east or west from a true great-circle line.

PSP_001558_1325 and PSP_001593_2635: These dune fields are striking, forming incredible patterns.

PSP_001582_2245: Looking like a super-sized area of dried mud, the polygonal cracks in this image are amazing.

Updated (2008-Apr-10)

Here are 66 false-color images from the 1400 orbit range.

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PSP_001406_2680 looks like the higher relief was saturated (too bright for the camera settings), possibly due to CO2 frost cover.

PSP_001432_2015 is really cool; it’s on the edge of Olympus Mons, on the steep scarp leading to the much more gradual rise of the shield volcano. The rippled rolling dunes in PSP_001432_2610 are in striking contrast to the rocky floors between them. Check out the amazing slot canyons fractures along the left side in PSP_001440_2175.

The atmospheric haze in PSP_001444_2610 is incredible, though it does screw up the color registration on the bottom half of the image. This is 30 degrees East of the aforementioned dune location, but the same type of terrain. On some of these images, there will be CTX (Context camera) images. With similar haze conditions, over on UnmannedSpaceflight.com, Nirgal shows a colorized CTX image from MRO orbit 3624 for which there is a HiRISE view.

There are so many other great images in this set. The Holden Crater image deserves special mention. This area is on the candidate list for MSL, as mentioned in a previous post. A stereo print was made of this region at about the same resolution you see here; it was amazingly sharp, like looking into a scale model or diorama.

Again, feel free to post your favorites here in the comments.

Updated (2008-Apr-10)

If HiRISE is like, well, a high rise, then each orbit range is a floor. The thirteenth floor consists of observations in the range 1300 to 1399. These were our first images of Primary Science Phase.

Click the link below to view a gallery of 50 HiRISE images in the 1300 range, drawn from our online PDS data node. The RGB browse is shown in the window, linked to the full JPEG 2000 using the IAS viewer. The RGB browse scale image is usually scaled down by a factor of 8–in both horizontal and vertical directions–from the JP2 product. So the browse image shows you around 1/64th of the color data: there are vast and beautiful scenes that can only be seen in full by zooming in with IAS. Nevertheless, if you have some time, this is a good way to explore a set of images and get an overall idea of what there is to see.

View Images

Most images are several times taller than your computer screen, so make sure to scroll through each one. Let us know which images are you favorite via the comments form below.

Updated (2008-Apr-10)

As described in a previous post, HiRISE color images are made by combining images in three different wavelengths of light, infrared (IR), red (RED) and blue-green (BG). The incoming light from the surface of Mars is separated by a filter into these three parts of the spectrum. The detectors that receive those wavelengths of light then build up the three separate images of the same place on the surface. The IR and BG detectors are above and below the RED detectors in the HiRISE focal plane, so they are imaging the same place, but at slightly different times. In order to create the color products, the three separate images have to be stacked one on top of the other. Lining up these images perfectly with each other is called coregistration.

This process seems simple in concept, but in practice it is quite complicated. There are three factors to account for:

• Relative timing
• Pixel binning
• Spacecraft jitter

First of all, HiRISE nearly always uses different resolutions for each color. For instance, RED might be at a scale of 25 cm/pixel (bin1) while IR and BG are at 1 meter/pixel (bin4). This “binning” minimizes the amount of data that has to be sent back to Earth, which is the most important constraint that HiRISE needs to deal with. Another reason for binning color is to improve the signal-to-noise ratio (SNR). This means that you can get a better signal by combining pixels at the expense of spatial resolution. In order to get the binned IR and BG images to line up properly, they must be enlarged to match the dimensions of the RED image. For example: RED is bin1, IR and BG are bin2. The RED image will be 2000 pixels wide by 40,000 pixels long (for example), the corresponding IR and BG images will be 1000 pixels wide by 20,000 pixels long. So the first step is to make the dimensions of all three images match.

Now the relative timing is easy to take care of. The start time of all images is a known quantity, and does not change from image to image. We know exactly when the BG detector starts imaging, followed by the RED detector, followed by the IR detector. So the beginning of each image is offset by a fixed amount. Once the images are shifted by this fixed offset (accounting for binning), they will be approximately lined up.

This brings us to the third factor in coregistering images — spacecraft jitter. Because HiRISE is imaging at such a high spatial resolution and at great speed, tiny motions of the MRO spacecraft cause slight variations in where the surface features appear in each of the three color detectors. Imagine that HiRISE is taking a picture of a 1m sized boulder on Mars. If the rock shows up in line 100 of the RED image, and we have already accounted for the relative offsets of the detectors and for binning, then the rock should also show up in line 100 of the BG image. But say we look and it is actually in line 99. Now when we try to stack the two images, the objects in them won’t line up exactly. Our color processing software corrects for this by holding the RED image fixed, and adjusting the corresponding BG and IR images to match it precisely. This is not a perfect process, but most of the time it works extremely well.

Producing the color HiRISE products is not a trivial process. But it is to a point where the processing is automated so new data is released without delay. Enjoy the colorful view!

On Friday, HiRISE released over 1200 color observations. This was our first large release of the color products (not counting the 140+ images of MSL candidate sites released back in October). I was asked recently if our images look fairly similar to one another, or if they are all completely different. Well, you can now judge that for yourselves, but I feel the answer clearly tends toward the latter. The variety of terrain types on Mars is wider than you might have expected, and everywhere you look you’ll find something spectacular.

But I’d like to showcase one image in particular. Within this single image, there is a remarkable progression of landforms, in a view running down a small portion in the interior of Valles Marineris, the “Grand Canyon of Mars.” Here are a selected set of sub-images from the RGB color product; each thumbnail links to a larger view. All of the original products are available at our website.

At the top of the image is a flat, cratered plain, very much what one thinks of as typically Martian. The edge is abrupt, leading immediately to a steep descent crossing multiple layers of bedrock. The accumulating aprons of debris are channeled down between rocky ridges.

A number of boulder tracks are visible, remnants of mighty tumbles. You can follow one of these tracks for something like a kilometer down into the middle portion of the image. Here is a small part of this track.

Farther down, a network of scalloped terrain has formed in what must be a transition zone from the upper, steeper section and the lower, flatter step. What’s interesting to me about this section is, as shown in the image below, the scalloped edges form a stunning pattern of bifurcation.

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Each HiRISE image has a color strip in the central portion of the image. That strip is comprised of three color wavelengths, blue-green, red and near infrared. Let’s clarify some terms first. RED refers to the visible wavelength portion of the spectrum in which the full-width HiRISE images are taken. These look black and white, not red, because they are displayed in grayscale. But we call them RED images. The other two colors seen by the HiRISE camera are in the visible blue-green (called BG) and invisible near infrared (often called NIR, but we refer to it here as IR).

The magic happens when we succeed at coregistering the IR and BG to the RED parts of the image to produce the center strip, false color images. More about this in an upcoming post. The maximum width of a color image is 4048 pixels. Some HiRISE images are 100,000 pixels long, which makes for a very long skinny image. These are affectionately dubbed “color noodles” by the HiPI (PI=Principle Investigator).

The image below illustrates where the color portion of the image is located. The zoomed in part of the same image just shows more clearly how the colors can offer more detailed geologic information than is available in the RED (black and white) image. For detailed information about the use of the color products and how they can be interpreted for scientific purposes, please refer to “Information for Scientific Users of HiRISE Color Products”

Is this what Mars really looks like? The images are not true color. The three color images taken by HiRISE are coregistered and stacked on top of each other. Then each color layer is assigned to red, blue or green, because those are the colors that are projected on your screen. So you can see how the word “color” becomes quite confusing. First, red is black and white. Then, we have all those I’s R’s and G’s and B’s! The color in HiRISE color products is really false color, because we are assigning a visible color to one that is invisible to human eyes. Also, there are only three wavelengths of light, not the full visible spectrum we are used to seeing. The RGB products are more similar to “natural” color. Even with HiRISE’s limited color capability, there is still an incredible amount of information gained by having the two extra wavelengths.

Why is there a garish green strip along the right side of the color image (left side in the nomap products)? You will notice this in some of the HiRISE color products. It will be apparent in the IRB, but not the RGB products. This is due to one half of the IR10 CCD having electronics issues during the earlier part of the mission. This problem was resolved for most cases, so that later images have both channels of IR10 — no green strip. Some of the earlier images were also able to be reprocessed to restore the missing IR information.

What is the difference between “RGB” and “IRB”? The RGB products are different than the IRB products in that the IR channel has been replaced by a “synthetic blue” layer that creates an image that is somewhat closer to natural color. In many of the images, the infrared band does not contribute a lot of information. The bands in this product have also been stretched to provide better contrast. In other words, the RGB images are more aesthetic. The IRB product is a science product. It contains the IR, RED and BG layers.

In the IAS viewer, you can turn the bands on and off to see what information each one contributes to a particular image. Use this button to switch from color to grayscale. This dialogue will also allow you to switch the color assigned to each band. The way the images are stacked in the HiRISE images goes like this:

Changing two bands to display the same color will show what kind of information is contributed by each band.

Below is a detail from PSP_004052_2045 showing the IRB color overlaid on the RED image. It is a beautiful example of how the color available in HiRISE images gives us new information that aids in interpreting the images. They are also just plain beautiful.

With the color images, dynamic range becomes more important then ever before. The DRA (Dynamic Range Adjustment) options of the IAS viewer are a great boon when looking at these images.

DRA performs what image processing folks call a “stretch.” A stretch takes some range of pixel values from the file and maps it onto a new range for the screen. To take an example, consider an image that appears over-exposed: much of the information is in the upper range of pixel values and you will have trouble distinquishing any detail. If the over-exposed pixels are not completely saturated (i.e. they don’t all have the maximum value) then a stretch that reduces brightness can reveal this otherwise hidden detail.

HiRISE has a very high signal-to-noise ratio, and our targeting specialists do a very good job choosing camera settings (which they do individually for each and every image) so completely saturated pixels are very rare.

But this also means that a stretch that works well over the entire image (a global stretch) may not be the best, the optimal stretch, for any one sub-image area that you are viewing. This is where the Auto DRA function in IAS becomes critical.

The button (shown below) is located on the right-hand side of the toolbar. A single click will do a stretch based only on the pixels you are viewing. This can bring out detail in shadow–amazingly, there is enough ambient light scattering around in the thin atmosphere to illuminate those scenes (and HiRISE is sensitive enough to pick enough of it up). It can also bring out detail in bright areas of over-exposure. For the color images in particular this can make things look a whole lot better.

Another factor plays a part in this. By default, the IAS viewer performs a global DRA when the image is loaded. As seen in the screenshot below, there are areas in our image that can skew the stretch. The large red rectangle is an area where the red CCDs start imaging before the blue-green. The IRB images often will have a cyan region where one of the IR CCDs was too noisy. We have elected to keep these areas in our images.

When in a sub-area, hit the Auto DRA button and the image should be drastically improved, as you can see in this final screenshot.

DRA early and often!