Is it acceptable if radar Mosaic is performed on SAR Ortorectified images rather than SLC?
Also, wondering which resampling method is recommended for SAR Mosaic ?
Also, What exactly “Weighted Average of overlap” and “Normalize” do? Is it recommended over already orthorectified SAR images?
I personally would also mosaic after I performed all other pre-processing steps. Only then you can assume maximum geometric accuracy.
Resampling is needed when pixels of two adjacent scenes are not completely registered towards each other. One of the two rasters has then shifted to the pixel registration of the other.
Nearest neighbor doesn’t change your values and assigns the pixel value of closest position
Cubic convolution takes even more pixels into consideration. It changes your radiometry the most and is mostly suited for resampling between different pixel sizes:
As you are mostly working on water detection I would recommend the nearest neighbor resampling as it leaves the pixel values as they are so you don’t lose information at the border between water and land.
Weighted average means that pixels of both rasters are averaged at overlapping parts in your data. This is only advised if you did proper radiometric calibration but it clearly prevents harsh borders between your images.
Normalize changes the pixel values of your whole image so that both of your input rasters show the same histogram after mosaicing. It is good if you didn’t work with absolute pixel values for thresholds (e.g. -15 db) before. But consider that your previously defined image statistics no longer apply after mosaicing with normalization.
Since for SAR TC I used the Bilinear, is it acceptable if I do the same for the mosaicing on my sigma0 orthorectified images?
I have also checked “Weighted Average” and “Normalize”. All my water masking through db values are gonna be done on the mosaic outputs.
The values of the first mosaic now is a combination of positive and negative sigma0. Negative values are odd though! Plz correct me if I miss some points here.