![]() ![]() The “Mean GR Square Pixel”, which is 14.02 m in this case, is actually the mean value of the two ground spacing. The Sentinel-5P data I work on lately is typically organised on an irregular two-dimensional grid whose dimensions are scanline (along track dimension) and ground pixel (across track dimension). The two spacing won’t be exactly the same due to the integer limitation on the number of looks. its azimuth spacing will be approximately the same as its ground range spacing. This is because with this setting the multilooked image will have a roughly square pixel on the ground, i.e. Say you select “GR Square Pixel” in Multilook UI, the operator will suggest you with 6 for “Number of Range Looks” and 1 for “Number of azimuth Looks”. To get the ground range pixel spacing, it should be projected to the ground. The 2.3x8 m is slant range pixel spacing. Sometimes we want the mutilooked image has a roughly squared pixel on the ground. Then what is the mean GR square pixel? It is the pixel spacing on the ground. These can be seen in the metadata of the output product. With this solution, the full resolution Sentinel 2 image can be processed in less than 3. Afterwards, a machine learning classifier (we chose nive bayes) is applied to generalize from the subsampled pixels to reconstruct the whole scene. If you multilook the image with 2 azimuth looks and 8 range looks, then you will have a multilooked image with 14.1x2 m azimuth spacing and 2.3x8 m slant range spacing. To overcome this limitation we subsample the pixels randomly and apply the clustering in this subsample. Say you SLC product has a 14.1 m azimuth spacing and 2.3 m slant range spacing.
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