I have created a DEM, DSM, and nDSM from a LiDAR Point Cloud using SAGA-GIS. I extracted the appropriate returns from the point cloud, then converted to grid, and then closed gaps to fill in any nodata regions.
I'm finding that my resultant rasters are still pretty noisy. For example, a region of pixels in the DSM that is obviously a tree or cluster of trees has several pixels with a very similar value to ground instead of the surrounding higher value pixels.
I've tried doing a majority filter on the DSM, but it didn't help.
Any suggestions for other filters that might help create more contiguous regions of pixels?
Best Answer
I got good results with mDenoise. This tool uses the Sun's denoising algorithm which removes noise without filtering sharp edges like ridges or peaks. Good for mountainous areas especially high mountains.
You can define the threshold and the number of iterations. You have to try something around to get the best result.
Before denoising ASTER GDEM2:
After denoising ASTER GDEM2 (60 iterations [-n], threshold 0,98 [-t] ):