Visibility Detection in Time Series of Planetscope Images
Tristan Dagobert, Jean‐Michel Morel, Carlo de Franchis, Rafael Grompone von Gioi · 2019
This article addresses the problem of estimating scene visibility in time series of satellite images. We are especially focused on satellites with few spectral bands and high revisit frequency. Our approach exploits the redundancy of information acquired during these revisits. It is based on an unsupervised algorithm that tracks local ground textures across time and detects ruptures caused by opaque clouds, haze, cirrus and shadows. Experiments have been carried out on 18 PlanetScope time series covering various locations. These time series come with hand-made labeled ground truth. We compare our results with those of the PlanetScope algorithm and demonstrate the effectiveness of the proposed method : success rates of 94% and 84% are reached for the visible and occulted regions classification.