Solution uses provided NITF files, by explicitly cropping using "left-image-crop-win" and "right-image-crop-win" parameters our region of interest. Optionally, post-process result in order to exploit scoring function described below as well. The first step is to compute the offsets of each image. It turns out that different pairs of images can produce results of very similar structure but up to around 10 meters apart from each other. In order to find offsets of all point clouds we use a naive greedy algorithm.
We assume that the first offset is 0. Then, iteratively we add next point cloud at offset that minimizes the average error between existing set of point clouds and new one. After adding all of the point clouds, we further improve the offsets by repeating the process one more time. During the second iteration we only adjust the offsets of existing point clouds by removing them and adding them again. One small detail that is worth mentioning is that when doing any calculation on point clouds, we convert them to 2D grid, where each tile is 30cm x 30cm.
While this may lose small amount of information, effect is pretty much negligible compared to the amount of noise obtained from merging over a dozen of different point clouds.
Since those 2D grid-based point clouds can be viewed as grayscale 2D images with some pixels missing , sometimes I'm refering to those point clouds simply as images and particular points in them as pixels.
The last step is to compute the final merged point cloud. Again, my final result is simply a 2D image with possibly some pixels missing. Each pixel height of a tile is computed as a median of pixels. In my final submission, I slightly deviated from this and when there's more than 5 pixels available, instead of using median, I use mean of the middle pixels.
It's hard to tell if that's a better idea. The premise behind it is that mean after dropping potential outliers might be better height estimation for places where it's relatively easy for photogrammetry to generate results. Most of the paramaters that I use in Stereo Pipeline are default, except for these: --subpixel-mode 2 --corr-kernel 11 11 --filter-mode 2 --rm-threshold 1 --prefilter-kernel-width 1.
Changing subpixel-mode was obvious choice. The reason for changing other parameters was that provided satellite images were quite sharp and decent quality. At the same time, they contained a lot of small-scaled features and highly-variable terrain, which most probably is not the standard usage for Stereo Pipeline. Based on this, I assumed I should reduce the amount of blur in preprocessing.
At the same time, it should be advantageous to filter out way more points than usual, since we would like to limit our point clouds to contain only high-quality results.
Scoring function consisted of two separate components: completeness and accuracy. Both of them could be exploited i. Which means that it was possible to artificially reduce RMSE improve accuracy , by not returning values for low-confidence places. On the other hand by doing so, we reduce completeness, so the goal was to find the balance between those two.
The easiest was to accomplish this, was to not return point clouds for areas around the edges of buildings. Or to be more exact, to check if the difference between maximum and minimum expected height in close vicinity was above some fixed threshold. Script setup. Apart from installing some libraries through "apt-get" it also downloads StereoPipeline and puts it in home directory.
It doesn't have to stay there, but remember that "StereoPipeline If you don't need, you can just comment those lines out. Running the solution is a little bit more complicated. The DigitalGlobe constellation, with the addition of WorldView-3, enables mining customers to detect multiple minerals with low false positives. This helps save time, resources, and even lives through increased efficiency and more cost-effective exploration.
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Disclaimer and data information. To simulate new spatial and spectral capabilities that will be enabled by the WorldView-3 WV-3 satellite following a successful launch and calibration, we have processed a series of surrogate data sets from a variety of sources. Because of the lack of satellite-based imaging sensors of suitable spatial and spectral resolution, airborne platforms were used. These data were resampled both spatially and spectrally, using engineering knowledge of the WV-3 sensor characteristics, in order to create simulations of WV-3 data.
These simulations are notional and represent our best efforts to demonstrate a future capability that does not yet exist. In order to simplify the presentation of the combined sensor bands, the SWIR bands are upsampled using nearest neighbor to 1. Two images are provided, derived from imagery collected by aerial platforms and sensors. This material is based in part upon work supported by the National Science Foundation under Grant No. Best in class resolution. Enabling more answers from imagery.
Lift the veil of smoke and fog with SWIR. Creating consistent imagery. Checks a system to see how it applies GEO data around 00, File also contains image comments.
Can the system handle a JPEG-compressed x 8-bit mono image that is non-divide by 8, and file also contains image comments? Checks a JPEG-compressed x 8-bit mono image with a corrupted restart marker occurring too late. Checks a JPEG-compressed x 8-bit mono image with a corrupted restart marker occurring too early.
Checks a 1-bit mono with mask table having 0x00 black as transparent with white arrow. Checks all run lengths on a bi-level compressed at 1D and x FAX imagery.
Checks to see if a system can render CGM Text in the proper location. Checks to see if the system can render CGM polygon sets properly and two polygons that do not intersect.
Checks to see if the system renders a basic Circle. Checks for rendering CGM ellipses with edge width of Checks for rendering CGM polylines types 1 through 5. Checks an IMODE S image with a data mask subheader, the subheader with padded pixels, having a color value of 0x00, 0x00, 0x00 displaying as transparent, and 3x3 blocks. Checks for rendering CGM polygons with hatch style 5.
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