Temporal Repetition Detection for Ground Visibility Assessment

Abstract

Abstract. Assessing ground visibility is a crucial step in automatic satellite image analysis. Nevertheless, several recent Earth observation satellite constellations lack specially designed spectral bands and use a frame camera, precluding spectrum-based and parallax-based cloud detection methods. An alternative approach is to detect the parts of each image where the ground is visible. This can be done by comparing locally pairs of registered images in a temporal series: matching regions are necessarily cloud free. Indeed, the ground has persistent patterns that can be observed repetitively in the time series while the appearance of clouds changes at each date. To detect reliably the “visible” ground, we propose here an a contrario local image matching method coupled with an efficient greedy algorithm.

Relative Radiometric Normalization Using Several Automatically Chosen Reference Images for Multi-Sensor, Multi-Temporal Series

“…Rather than a single image spectral cloud detector such as [Zhu et al, 2015], we use a ground visibility detector [Grompone von Gioi et al, 2020]. This is a simple and fast detector perfectly adapted to our problem.…”

“…The ground visibility detector [Grompone von Gioi et al, 2020] works as follows: given a set of registered images, the ground visibility masks for the images are all initialized as not visible; then all pairs of images are compared locally. The comparison is performed on the angles of the image gradients, which means that it is insensible to affine modifications of the contrast.…”

Ground Visibility in Satellite Optical Time Series Based on A Contrario Local Image Matching

“…Instead of detecting clouds, an alternative approach is to detect ground visibility, that is, the parts of each image where the ground is visible [7,6,16]. This can be done by comparing the corresponding parts of a time series and selecting matching regions.…”

“…This procedure, coupled with a final morphological area filter, results in an effective ground visibility detection algorithm for satellite time series. This method was first introduced in [16].…”