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Scene distortion detection algorithm using multitemporal remote sensing images

A.M. Belov1, A.Y. Denisova1

1 Samara National Research University, Moskovskoye Shosse 34, 443086, Samara, Russia

 PDF, 1528 kB

DOI: 10.18287/2412-6179-2019-43-5-869-885

Pages: 869-885.

Full text of article: Russian language.

Abstract:
Multitemporal remote sensing images of a particular territory might include accidental scene distortions. Scene distortion is a significant local brightness change caused by the scene overlap with some opaque object or a natural phenomenon coincident with the moment of image capture, for example, clouds and shadows. The fact that different images of the scene are obtained at different instants of time makes the appearance, location and shape of scene distortions accidental. In this article we propose an algorithm for detecting accidental scene distortions using a dataset of multitemporal remote sensing images. The algorithm applies superpixel segmentation and anomaly detection methods to get binary images of scene distortion location for each image in the dataset. The algorithm is adapted to handle images with different spectral and spatial sampling parameters, which makes it more multipurpose than the existing solutions. The algorithm's quality was assessed using model images with scene distortions for two remote sensing systems. The experiments showed that the proposed algorithm with the optimal settings can reach a detection accuracy of about 90% and a false detection error of about 10%.

Keywords:
accidental scene-distortions detection, remote sensing image fusion, super-pixel image segmentation, anomaly detection.

Citation:
Belov AM, Denisova AY. Scene distortion detection algorithm using multitemporal remote sensing images. Computer Optics 2019; 43(5): 869-885. DOI: 10.18287/2412-6179-2019-43-5-869-885.

Acknowledgements:
The work was partly funded by the Russian Foundation for Basic Research under RFBR grants ## 18-07-00748 a, 16-29-09494 ofi_m and under the project “Creation of a Geographic Information Hub of Big Data”, carried out as part of the Competence Center Program of the National Technological Initiative “Big Data Storage and Analysis Center”, supported by the Ministry of Science and Higher Education of the Russian Federation under an agreement between M.V. Lomonosov Moscow State University and the Project Support Foundation of the National Technology Initiative, dated December 11, 2018 No. 13/1251/2018.

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