Loading libraries and defining locale environment. This isnt extremely accurate but allows masking in order to avoid overcorrection at a later stage. Note however that you might need another lowpass filtering run to reduce artefacts that originate often from edges in your DEM. ATCOR allows users to prepare their data for analysis, such as GCP collection, segmentation, classification, or extraction of vegetation indices. ATCOR depends heavily on the quality of the visibility map and the accuracy of the atmospheric tables based on MOTRAN1 code , as well as on the quality of the haze-free scene and terrain derivatives. The specified file must match exactly the geocoding of the input image and the mask for cloud and haze. This parameter is composed of a keyword and an optional filter size. The effect is a result of atmospheric scattering, and depends on the reflectance contrast between a target pixel and its large-scale neighborhood, and decreases with wavelength.
Create cloud, haze, and water masks from satellite imagery
Jena Copter Laboratories
I learned a basic trick when masking that helped me out. It will be a duh to some of you, but for me, I was happy to figure it out. Say you have some close up pine trees and distant very hazy forest behind, and you want to clear the haze. So, you are drawing a mask around the branches of the near pines. If you were to use your current mask level on the inner branches, your branches go too dark.
Input1: Blue or green image channel 0. The image channel in the input file that contains the blue sensor band. If the blue band is not available, the green band can be used instead. The image channel in the input file that contain the shortwave infrared SWIR sensor band. This parameter specifies, potentially with the value of Source Background Values , which pixels in the source image are to be considered background NoData pixels.