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Scene distortion detection on a series of multi-temporal remote sensing images of the same territory
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Paper Abstract

Multi-temporal Earth remote sensing images of the same territory may include random scene-distortions coming from the different natural phenomena, for example, clouds or shadows. These distortions are time dependent and may appear only in several images of the analyzed image set. Thus, they define irrelevant image parts that should be eliminated in the further data fusion process. In this article, we suggest an algorithm for detecting such scene distortions using a series of multi-temporal remote sensing images. The algorithm is based on super-pixel segmentation and anomaly detection methods. The algorithm produces a mask of random scene-distortions for each of the images in the analyzed series. The resulting mask could be used to take into account only the scene-relevant parts in the data fusion methods. The proposed approach allows processing images with different spectral and spatial sampling simultaneously that is very useful for multi-sensor data fusion. We tested an algorithm quality by modeling a series of multispectral images with different parameters of spectral and spatial sampling under the different conditions of cloudiness and cloud shadows as an example of random distortions in the scene. As a result, it was shown that the algorithm provides the accuracy of scene distortion detection about 90% and false detection about 10%.

Paper Details

Date Published: 3 January 2020
PDF: 10 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730S (3 January 2020); doi: 10.1117/12.2557178
Show Author Affiliations
A. M. Belov, Samara National Research Univ. (Russian Federation)
A. Y. Denisova, Samara National Research Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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