MULTIDIMENSIONAL STATISTICAL MODEL FOR DETECTING OIL POLLUTION SITES BASED ON SATELLITE IMAGERY-Reference-Cited by-同舟云学术

MULTIDIMENSIONAL STATISTICAL MODEL FOR DETECTING OIL POLLUTION SITES BASED ON SATELLITE IMAGERY

Published:2021-05-21 Issue:1 Volume:4 Page:11-16
ISSN:2618-981X
Container-title:Interexpo GEO-Siberia
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Author:

Guliyev Alovsat Shura¹,Khlebnikova Tatiana A.²

Affiliation:

1. State Oil Company of Azerbaijan Republic (SOCAR)

2. Siberian State University of Geosystems and Technologies

Abstract

The article considers an algorithm for determining the statistical model from several inhomogeneous images of the Earth's surface obtained by different sensors (optoelectronic scanning device, synthetic aperture radar (SAR)) over the sea areas. The object of the study are the methods of remote sensing of the Earth used for detection and mapping of oil spills. The aim of the research was to perform testing for a possible variation of the statistical model inside a non-uniform sliding window based on a semi-automatic approach. The proposed algorithm makes it possible to determine the spatial extent of oil production sites and oil pollution in offshore waters using multi-time RSA data and a multi-zone combined image with a spatial resolution of 10 m. First, homogeneous regions are analyzed in the image, and then the model of the analysis zone is expanded to the more general case of inhomogeneous regions that are observed in the analysis windows.

Publisher

Siberian State University of Geosystems and Technologies

Subject

Industrial and Manufacturing Engineering

Reference9 articles.

1. M. Chabert and J.-Y. Tourneret, Bivariate Pearson distributions for remote sensing images, in Proc. IEEE Int. Geosci. Remote Sens. Symp., Vancouver, Canada, July 2011, pp. 4038-4041 [Electronic resource]. - Mode of access: https://ieeexplore.ieee.org/document/6050118 (дата обращения 21.01.2021).

2. G. Mercier, G. Moser, and S. B. Serpico, Conditional copulas for change detection in heterogeneous remote sensing images, IEEE Trans. Geosci. Remote Sens., vol. 46, no. 5, pp. 1428-1441, May 2008 [Electronic resource]. - Mode of access:https://ieeexplore.ieee.org/abstract/document/4481231(дата обращения 21.01.2021).

3. M. S. Allili, D. Ziou, N. Bouguila, and S. Boutemedjet, Image and video segmentation by combining unsupervised generalized Gaussian mixture modeling and feature selection, IEEE Trans. Circuits Syst. Video Technol., vol. 20, no. 10, pp. 1373-1377, Oct. 2010 [Electronic resource]. - Mode of access: https://ieeexplore.ieee.org/document/5580019 (дата обращения 21.01.2021).

4. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, Multimodality image registration by maximization of mutual information, IEEE Trans. Med. Imag., vol. 16, no. 2, pp. 187-198, 1997 [Electronic resource]. - Mode of access:https://ieeexplore.ieee.org/document/563664 (дата обращения 21.02.2021).

5. M. Chabert, J.-Y. Tourneret, V. Poulain, and J. Inglada, Logistic regression fordetecting changes between databases and remote sensing images, in Proc. IEEE Int.Geosci. Remote Sens. Symp., Honolulu, USA, 2010, pp. 3198-3201 [Electronic resource]. - Mode of access:https://ieeexplore.ieee.org/document/5649669 (дата обращения 21.02.2021).