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Scene distortion detection algorithm using multitemporal remote sensing images

A.M. Belov1, A.Y. Denisova1

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.

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%.

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

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.

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.


  1. Pasetto D, Arenas–Castro S, Bustamante J, Casagrandi R, Chrysoulakis N, Cord AF, Dittrich A, Domingo-Marimon C, El Serafy G, Karnieli A, Kordelas GA, Manakos I, Mari L, Monteiro A, Palazzi E, Poursanidis D, Rinaldo A, Rinaldo S, Terzago S, Ziemba A, Ziv G, Kordelas GA. Integration of satellite remote sensing data in ecosystem modelling at local scales: Practices and trends. Methods in Ecology and Evolution 2018; 9 : 1810-1821.
  2. Anshakov GP, Raschupkin AV, Zhuravel YN. Hyperspectral and multispectral Resurs-P data fusion for increase of their informational content. Computer optics 2015; 39(1): 77-82.
  3. Belov AM, Denisova AY. Spectral and spatial super-resolution method for Earth remote sensing image fusion. Computer Optics 2018; 42(5): 855-863. DOI: 10.18287/2412-6179- 2018-42-5-855-863.
  4. Denisova AYu, Myasnikov VV. Algorithms of linear spectral mixture analysis for hyperspectral images using base map. Computer Optics 2014; 38(2): 297-303.
  5. Sun L, Mi X, Wei J, Wang J, Tian X, Yu H, Gan P. A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths. ISPRS journal of photogrammetry and remote sensing 2017; 124: 70-88.
  6. Thompson DR, Green RO, Keymeulen D, Lundeen SK, Mouradi Y, Nunes DC, Castaño R, Chien SA. Rapid spectral cloud screening onboard aircraft and spacecraft. IEEE Transactions on Geoscience and Remote Sensing 2014; 52: 6779-6792.
  7. Zhu X, Helmer EH. An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions. Remote Sensing of Environment 2018; 214: 135-153.
  8. Li J, Menzel WP, Yang Z, Frey RA, Ackerman SA. High-spatial-resolution surface and cloud-type classification from MODIS multispectral band measurements. Journal of Applied Meteorology 2003; 42: 204-226.
  9. Luo S, Li H, Shen H. Shadow removal based on clustering correction of illumination field for urban aerial remote sensing images. 2017 IEEE International Conference on Image Processing (ICIP) 2017; 485-489.
  10. Mostafa Y. A review on various shadow detection and compensation techniques in remote sensing images. Canadian Journal of Remote Sensing 2017; 43: 545-562.
  11. Movia A, Beinat A, Crosilla F. Shadow detection and removal in RGB VHR images for land use unsupervised classification. ISPRS Journal of Photogrammetry and Remote Sensing 2016; 119: 485-495.
  12. Champion N. Automatic detection of clouds and shadows using high resolution satellite image time series. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 2016; 41(B3): 475-9.
  13. Breunig MM, Kriegel HP, Ng RT, Sander J. LOF: identifying density-based local outliers. ACM sigmod record 2000; 29(2): 93-104.
  14. Domingues R, Filippone M, Michiardi P, Zouaoui J. A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition 2018; 74: 406-421.
  15. Zhao H, Jia G, Li N. Transformation from hyperspectral radiance data to data of other sensors based on spectral superresolution. IEEE Trans. Geosci. Remote Sens. 2010; 48(11): 3903-3912.
  16. Farsiu S, Robinson MD, Elad M, Milanfar P. Fast and robust multiframe super resolution. IEEE transactions on image processing 2004; 13(10): 1327-1344.
  17. Student. The probable error of a mean. Biometrika 1908; 1-25.
  18. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. SLIC Superpixels. EPFL Technical Report 2010; 149300: 1-15.
  19. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence 2012; 34(11): 2274-2282.
  20. Hartigan JA, Wong MA. Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics) 1979; 28(1): 100-108.
  21. Margulis D. Photoshop LAB color: The canyon conundrum and other adventures in the most powerful colorspace. Peachpit Press; 2005.
  22. Vane G, Green RO, Chrien TG, Enmark HT, Hansen EG, Porter WM. The airborne visible/infrared imaging spectrometer (AVIRIS). Remote sensing of environment 1993; 44(2-3) : 127-143.
  23. Bartoš M. Cloud and Shadow Detection in Satellite Imagery. Master Thesis. Czech Technical University in Prague Faculty of Electrical Engineering. Prague, 2017; 1-57.
  24. Xu M, Jia X, Pickering M, Plaza AJ. Cloud removal based on sparse representation via multitemporal dictionary learning. IEEE Transactions on Geoscience and Remote Sensing 2016; 54(5): 2998-3006.
  25. Schowengerdt RA. Remote sensing: models and methods for image processing. Elsevier; 2006.
  26. Spot-7 Satellite Sensor. Source: <https://www.satimaging­corp.com/satellite-sensors/spot-7/>.
  27. Russian remote sensing constellation by date (01.03.2015). Geomatics 2015; 1: 106-112.
  28. Farsiu S, Elad M, Milanfar P. Multiframe demosaicing and super-resolution of color image. IEEE Transactions on Image Processing 2006; 15: 141-159.
  29. Matlab. Source: <https://www.mathworks.com/pro­ducts/matlab.html> .
  30. Janssens JHM, Flesch I, Postma EO. Outlier detection with one-class classifiers from ML and KDD. Proceedings of the Eighth International Conference on Machine Learning and Applications 2009; 147-153.


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