[IPOL announce] new article: Temporal Repetition Detector for Time Series of Spectrally Limited Satellite Imagers

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2020/245/

Tristan Dagobert, Rafael Grompone von Gioi, Jean-Michel Morel, and Carlo 
de Franchis,
Temporal Repetition Detector for Time Series of Spectrally Limited 
Satellite Imagers,
Image Processing On Line, 10 (2020), pp. 62–77.
https://doi.org/10.5201/ipol.2020.245

Abstract
This article addresses the problem of estimating scene visibility in 
time series of satellite images. It focuses 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 mainly by opaque clouds and in some cases by 
haze, cirrus and shadows. Experiments have been carried out on 18 
PlanetScope image times series of various locations. These time series 
come with hand-made ground truth labels that are published together with 
this paper. We compare our results with the Unusable Data Masks (UDM) 
that Planet provides together with the images, and demonstrate the 
effectiveness of the proposed method: success rates of 97.78% and 89.36% 
are reached for the visible and occluded regions classification. This 
article is related to the following publication: [Tristan Dagobert, 
Jean-Michel Morel, Carlo de Franchis and Rafael Grompone von Gioi, 
Visibility detection in time series of Planetscope images, IEEE 
International Geoscience And Remote Sensing Symposium, 2019].