DSCOVR |
DSCOVR |
Jan 6 2006, 08:55 PM
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#101
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Senior Member Group: Members Posts: 2454 Joined: 8-July 05 From: NGC 5907 Member No.: 430 |
ADMIN NOTE: Please note that this topic was unavoidably poltical before the 'No Politics' rule. Please restrict future comments to the mission/spacecraft/news updates etc.
WHAT'S NEW Robert L. Park Friday, 6 Jan 06 Washington, DC DEEP SPACE CLIMATE OBSERVATORY KILLED. http://bobpark.physics.umd.edu/index.html -------------------- "After having some business dealings with men, I am occasionally chagrined,
and feel as if I had done some wrong, and it is hard to forget the ugly circumstance. I see that such intercourse long continued would make one thoroughly prosaic, hard, and coarse. But the longest intercourse with Nature, though in her rudest moods, does not thus harden and make coarse. A hard, sensible man whom we liken to a rock is indeed much harder than a rock. From hard, coarse, insensible men with whom I have no sympathy, I go to commune with the rocks, whose hearts are comparatively soft." - Henry David Thoreau, November 15, 1853 |
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Dec 10 2016, 08:14 PM
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#102
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Solar System Cartographer Group: Members Posts: 10229 Joined: 5-April 05 From: Canada Member No.: 227 |
How about... don't use the limb detection routine to locate the limb, use it plus a limb-fitting routine to get a best fit and use that to establish the central (sub-spacecraft) pixel. Use that central point plus a calculated radius based on range and geometry to fit the 'true limb' to the image. You can adjust some of the geometry parameters until they give optimum results.
More simply, you could use your existing routine but multiply by the fraction necessary to shrink the coastline map to fit the image. Once established that fraction should be fairly constant, or at least can be varied as a function of range. Phil -------------------- ... because the Solar System ain't gonna map itself.
Also to be found posting similar content on https://mastodon.social/@PhilStooke Maps for download (free PDF: https://upload.wikimedia.org/wikipedia/comm...Cartography.pdf NOTE: everything created by me which I post on UMSF is considered to be in the public domain (NOT CC, public domain) |
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Dec 10 2016, 09:21 PM
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#103
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Junior Member Group: Members Posts: 22 Joined: 7-January 13 Member No.: 6834 |
The limb detection is largely impacted by the uncertainties associated with the terminator. In some images, the pixels disapear when the sun is locally 10° above the horizon; and in other images they disapear when the sun is virtually 0° above the horizon !
In addition, it appears that a shrink/translate transform is not enough to compensate for all of the errors in the metadata, this is why I switched to optical flow that tried to fit the coastlines and other salient features (not an affine transformation). It somehow works, but requires significant tuning to run on a batch of several images. I can't afford to spend too much time to make it work on thousands of images with a very low error rate. I other words: More simply, you could use your existing routine but multiply by the fraction necessary to shrink the coastline map to fit the image. doesn't work.I'm really curious to know how the scientists can actually work with such errors. Do they do the same kind of corrective process (with hopefully more time than 2 hours during the week-end like us) ? Could we expect that they correct their calibrated data accordingly one day ? |
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Jan 24 2017, 06:11 PM
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#104
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Senior Member Group: Members Posts: 2530 Joined: 20-April 05 Member No.: 321 |
If I were to try to build a robust solution to this, I think I'd try the following. In large part, I think this follows more or less the algorithm that we people use in inspecting an image of the Earth.
Preparatory indexing: 1) Make an index of the shapes of coastlines at the resolution of ~ 5km/pixel. In particular, index segments where coastlines change orientation such as the Strait of Hormuz, the east coast of Somalia, the Baja peninsula, Gibraltar, southern Italy, Tierra del Fuego, Newfoundland, Michigan, etc. Processing a single image: 2) At the time the image was taken, make a list of the coastline segments that are located within ~60° of the sub solar point. Perform a transformation to adjust them to how they should appear from the direction of the Sun, which will approximate the geometry of DSCOVR. 3) Run edge detection on the image, excluding any edges that are bounded by white regions, which are probably clouds. 4) Match the detected segments against the projected indexed segments from (2). 5) If three or more segments are matched (possibly two that are far apart), you now have a good registration between the image and the Earth. Probably the tricky step is (4), but there's research on this. |
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