CRISM Corner |
CRISM Corner |
Guest_AlexBlackwell_* |
Sep 28 2006, 02:14 AM
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#1
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APL-Built Mineral-Mapping Imager Begins Mission at Mars
JHU/APL For Immediate Release September 27, 2006 See also A.J.S. Rayl's story at TPS. EDIT: I changed the topic title because, as I understand it from the press release, the cover was opened, not jettisoned. This post has been edited by AlexBlackwell: Sep 28 2006, 04:48 PM |
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Nov 4 2007, 01:03 PM
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#2
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Member Group: Admin Posts: 468 Joined: 11-February 04 From: USA Member No.: 21 |
Enhanced would be a nice way to put it. The true color comes out looking like this (unprojected):
...which isn't much to look at. The brightness range is very narrow, as is the spectral variation. So, to cut through the atmospheric contributions, and to make something a little more aesthetic, I've doing the equivalent of a giant global unsharp mask. The colors in the images I've posted so far are the difference between the pixel and a 150 pixel-wide blur. For now, I'm just trying to feel out what this dataset is going to have to offer. True color, or some variation there of, is just the beginning |
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Nov 5 2007, 03:26 AM
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#3
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Member Group: Members Posts: 267 Joined: 5-February 06 Member No.: 675 |
...For now, I'm just trying to feel out what this dataset is going to have to offer. True color, or some variation there of, is just the beginning I'd always thought that such a multi-spectral dataset would offer a nice opportunity for principal component analysis. Using a standard statistical package to extract the principal components, I imagine the first component would be something like the overall brightness variation, while the next two would represent the most significant spectral variations in the particular image and would presumably map the most significant mineral variations.From what I read, the CRISM folks are comparing the spectra they find to known terrestrial samples. Principal component mapping is more of an exploratory technique. At the end it might even be possible to compare the identified principal components with the known spectra to identify what minerals vary significantly in a given image. It's been years since I've done any statistics of this kind (and never with a dataset this big) but it seems like a plausible approach, even if a bit naïve. Steve M |
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