Measuring Visibility using Atmospheric Transmission and Digital Surface Model
This work addresses the need for objective visibility measurements in aviation to replace subjective human reports, though it appears incremental as it builds on existing image processing techniques.
The paper tackles the problem of automating visibility assessment for air traffic safety by using image processing to estimate atmospheric transmission and a digital surface model to compute depth, resulting in a global visibility estimation method validated on foggy images.
Reliable and exact assessment of visibility is essential for safe air traffic. In order to overcome the drawbacks of the currently subjective reports from human observers, we present an approach to automatically derive visibility measures by means of image processing. It first exploits image based estimation of the atmospheric transmission describing the portion of the light that is not scattered by atmospheric phenomena (e.g., haze, fog, smoke) and reaches the camera. Once the atmospheric transmission is estimated, a 3D representation of the vicinity (digital surface model: DMS) is used to compute depth measurements for the haze-free pixels and then derive a global visibility estimation for the airport. Results on foggy images demonstrate the validity of the proposed method.