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

Abstract

Assessing ground visibility is a crucial step in automatic satellite image analysis. Some Earth observation satellites are provided with spectral bands specially designed for cloud detection. An alternative approach is to detect ground visibility by comparing locally the 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 clouds change shape constantly. We describe here a ground visibility detection algorithm based on an a contrario local image matching method, coupled with an efficient greedy algorithm. Source CodeThe reviewed, reference source code for this algorithm is available from the web page of this article 1 . The algorithm is implemented in ANSI C language. Compilation and usage instruction are included in the README.txt file of the archive.

Statistical modeling of deep features reduces false alarms in video change detection

“…N T is related to the total number of candidate regions that can, in theory, be considered for evaluation. Inspired by the approach in [4], we will consider regions of any shape formed by 4-connected pixels. Regions of pixels with 4-connectivity are known as polyominoes [39,44].…”

“…Lisani and Ramis developed a method in [40] that applied an a-contrario methodology on a normal distribution for the detection of faces in images. In surveillance, a-contrario methods have been used mainly in the remote sensing field, [4,41], where the temporal difference between images is large, and no tracking of temporal objects is feasible. Grompone et al [4] proposed an a-contrario method based on a uniform distribution and a greedy algorithm to compute candidate regions, detecting visible ground areas in satellite imagery.…”

“…Change detection may also serve climate and humanitarian causes. Satellite image time series can be used to monitor urban development [4] of specific regions and the variability of gas concentrations in the atmosphere across time [5,6].…”

Image Forgery Detection Based on Noise Inspection: Analysis and Refinement of the Noisesniffer Method

“…To complete the formulation we still need to specify the family of regions to be tested. Instead of using rectangular macro-blocks as in the original Noisesniffer formulation [14], we consider more general connected regions, as in [17]. With this aim, we consider a square tessellation of the image, with squares of size l β × l β .…”

“…Instead, we propose a heuristic approach to reduce the number of regions that will be evaluated using the a contrario approach described in the previous section. The construction of such candidate regions is based on the greedy algorithm proposed by Grompone et al [17], and the modifications introduced in [35].…”