05. Yamashkin S. A., Yamashkin A. A. Improving the Efficiency of Remote Sensing Data Interpretation by Analyzing Neighborhood Descriptors.

UDK 528.8

DOI: 10.15507/0236-2910.028.201803.352-365

Improving the Efficiency of Remote Sensing Data Interpretation by Analyzing Neighborhood Descriptors

Stanislav A. Yamashkin
Senior Lecturer, Chair of Automated Systems of Information Processing and Control, National Research Mordovia State University (68/1 Bolshevistskaya St., Saransk 430005, Russia), Ph.D. (Engineering), ResearcherID: N-2939-2018, ORCID: https://orcid.org/0000-0002-7574-0981, This email address is being protected from spambots. You need JavaScript enabled to view it.

Anatoliy A. Yamashkin
Dean, Geography Faculty, National Research Mordovia State University (68/1 Bolshevistskaya St., Saransk 430005, Russia), D.Sc. (Geography), Professor, ResearcherID: N-2941-2018, ORCID: https://orcid.org/0000-0001-9995-8371, This email address is being protected from spambots. You need JavaScript enabled to view it.

Introduction. In evaluating the space-time structure of the Earth’s surface, the data of remote sensing of the Earth become more important. Increasing the effectiveness of space survey analysis tools is possible through studying the problem of obtaining an integrated space-time characterization of the state of lands. The purpose of this study is to improve the accuracy of the automated analysis of remote sensing data by taking into account the invariant and dynamic descriptors of the vicinity.
Materials and Methods. In order to improve the accuracy of the remote sensing data classification, a computation of complex space-time characteristics of the state of the lands was conducted based on the system analysis of data characterizing the dynamic and invariant states of the territory surrounding the geophysical site. The formalization of this process includes methods for calculating a set of numerical descriptors of the neighborhood: local entropy, local range, standard deviation, color moment, histogram of hues, and color cortege. A technique for calculating a complex descriptor based on the Fisher vector is described. To approbate the solution, a plan for the experiment was drawn up and a sample of the initial data was sampled.
Results. The approbation of the methodology and the algorithm developed on its basis, implemented as a set of programs, on the test polygon system showed a variation in the classification accuracy in the range of 81–89% (without regard to the neighborhood), and taking into account the neighborhood, it increases to 91–97%. It is revealed that a significant increase in the radius of the analyzed neighborhood leads to a decrease in the classification accuracy.
Conclusions. The application of the developed set of programs allows for the rapid implementation of modeling of spatial systems for the purpose of thematic mapping of land use and analyzing the development of emergency situations. The developed methodology for analyzing lands with regard to the descriptors of the neighborhood makes it possible to improve the accuracy of classification.

Keywords: interpretation of space images, remote sensing, land analysis, neighborhood descriptors, invariant property, dynamic property

For citation: Yamashkin S. A., Yamashkin A. A. Improving the Efficiency of Remote Sensing Data Interpretation by Analyzing Neighborhood Descriptors. Vestnik Mordovskogo universiteta = Mordovia University Bulletin. 2018; 28(3):352–365. DOI: https:///doi.org/10.15507/0236-2910.028.201803.352-365

Author’s contribution: С. S. A. Yamashkin developed the methods and algorithms for the analysis of lands and implementation of mathematical apparatus in form of a program system; A. A. Yamashkin analyzed the experimental results.

All authors have read and approved the final version of the paper.

Received 01.12.2017; revised 20.02.2018; published online 20.09.2018

REFERENCES

1. Kruse F. A., Lefkoff A. B. The spectral image processing system (SIPS) – interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 1993; 44:145–163.

2. Kohonen T. Self-organizing maps (third extended edition). New York, 2001.

3. Rosenblatt F. Analytic techniques for the study of neural nets. IEEE Trans. on Appl. and Industry. 1964; 83(74):285–292.

4. Breiman L. Bagging predictors. Machine Learning. 1996; 24(2):123–140.

5. Foody G. M., Mathur A. Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment. 2004; 93:107–117.

6. Polikar R. Bootstrap inspired techniques in computational intelligence: ensemble of classifiers, incremental learning, data fusion and missing features. IEEE Signal Processing Magazine. 2007; 24(4):59–72.

7. Polikar R. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine. 2006; 6(3):21–45.

8. Woods K., Kegelmeyer W. P. J., Bowyer K. Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1997; 19(4):405–410.

9. Yamashkin S. A., Yamashkin A. A., Frolov A. N. Basic information resources for optimisation of the regional land use. Mezhdunarodnyy nauchno-issledovatelskiy zhurnal = International Research Journal. 2016; 53(11-2):85–88. (In Russ.)

10. Yamashkin S. A. [Using ensemble-based systems in the analysis of remote sensing data for monitoring the state of lands and forecasting natural processes]. Fundamentalnyye i prikladnyye issledovaniya: problemy i rezultaty = Fundamental and Applied Research: Problems and Results. 2016; 28:71–77. (In Russ.)

11. Yamashkin S. A., Radovanovich M. M., Yamashkin A. A., Vukovich D. V., Frolov A. N.Using ensemble-based systems for the landscapes mapping. Geodeziya i kartografiya = Geodesy and Cartography. 2016; 7:42–49. (In Russ.)

12. Yamashkin S. A., Yamashkin A. A. [Modeling a synthetic landscape map]. Perspektivy razvitiya informatsionnykh tekhnologiy = Prospects for Development of Information Technology. 2016; 32:68–74. (In Russ.)

13. Yamashkin S. A. Monitoring of land and natural processes prediction based on analysis of invariant and dynamic parameters of the territory. Mezhdunarodnyy nauchno-issledovatelskiy zhurnal = International Research Journal. 2016; 51(9-2):110–112. (In Russ.)

14. Yamashkin A. A., Yamashkin S. A., Frolov A. N., Zarubin O. A. Synthetic landscape mapping in the regional GIS. Mezhdunarodnyy nauchno-issledovatelskiy zhurnal = International Research Journal. 2017; 58(04-1):54–56. (In Russ.)

15. Yamashkin A. A., Yamashkin S. A., Akashkina A. G. [GIS-modeling of landscape diversity]. Geodeziya i kartografiya = Geodesy and Cartography. 2013; 11:40–45. (In Russ.)

16. Yamashkin A. A., Yamashkin S. A. [Application of the algorithm for edge selection to the solution of the problem of modeling the borders of landscapes]. Vestnik Voronezhskogo gosudarstvennogo universiteta. Ser. Geografiya. Geoekologiya = Voronezh State University Bulletin. Series: Geography, Geoecology. 2013; 2:28–34. (In Russ.)

17. Yamashkin A. A., Yamashkin S. A., Klikunov A. A., Akashkina A. G., Shukshin Yu. S. Application of GIS in the analysis of morphological structure of the landscape. Vestnik Udmurtskogo universiteta. Ser.: Biologiya. Nauki o Zemle = Udmurt University Bulletin. Series: Biology, Earth Sciences. 2013; 6-3:115–122. (In Russ.)

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