Науковий вісник НГУ

Improvement of quality of the results of controllable classification of multispectral satellite images

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: To the 15th anniversary of Geoinformatic Systems Department

Last Updated on 27 November 2012

Published on 23 November 2012

Hits: 3404

Authors: І.N. Garkusha, Candidate of Sciences (Tech.), Associate Professor, Senior Lecturer of the GIS Department of the State Higher Educational Institution “National Mining University”, Dnipropetrovsk, Ukraine

Annotation:

The paper considers the problem of Earth surface types recognition, with similar spectral characteristics in multispectral satellite imagery obtained by the scanner QuickBird-2. A method of Earth surface types recognition, based on the method of building of areas by segments and spatial analysis of the pixel brightness is proposed. The paper presents the results of processing of satellite imagery of urban area by means of introduced algorithms.

Bibliography:

1. Russian source: Savelyev A.A. Sovremennye vozmozhnosti tematicheskoy obrabotki kosmicheskikh snimkov v programme ScanEx Image Processor / Savelyev A.A. // 4th International Conference “Earth from Space – the Most Effective Solutions”. – URL:http//www.conference.transparentworld.ru/docs/materials/011209/TrainingHall/processing1/savelyev.pdf.

2. Castleman K.R. Digital Image Processing. – New York: Prentice Hall Press, 1996. – 667 p.

3. Lei B.J. On feature extraction from images / Lei B.J., Hendriks E.A., Reinders M.J.T. // Technical Report, Deliverable 2.1.1.2A+B, MCCWS project, Information and Communication Theory Group. TU Delft, 1999. – 57 p.

4. Petrou M. Affine invariant features from the trace transform / Petrou M., Kadyrov A. // IEEE Trans. on Pattern Analysis and Machine Intelligence. – 2004. – Vol.26. – No.1. – P. 30–44.

5. Lowe D. Object recognition from local scale-invariant feature // Proc. Int. Conf. on Computer Vision. – 1999. – P. 1150-1157.

6. Razin I.V. Matematicheskaya model dlya ekspress-otsenki stepeni skhodstva izobrazheniy / Razin I.V., Teterin V.V. // Opticheskiy zhurnal. – 2001. – No.11. – P. 33–37.

7. Kruizinga P. Nonlinear operator for oriented texture / Kruizinga P., Petkov N. // IEEE Trans. on Image Processing. – 1999. – Vol.8. – No.10. – P. 1395-1407.

8. Portilla J. A parametric texture model based on joint statistics of complex wavelet coefficients / Portilla J., Simonceli E.P. // Int. J. of Computer Vision. – 2000. – Vol.40. – No.1. – P. 49‑71.

9. Russian source: Potapov A.S. Matematicheskiye metody i algoritmicheskoye obespecheniye analiza i raspoznavaniya izobrazheniy v informatsionno-telekommunikatsionnykh sistemakh. Vserossiyskiy konkursnyy otbor obzorno-analiticheskikh statey po prioritetnomu napravleniyu "Informatsionno-telekommunikatsionnye sistemy" / Potapov A.S., Gurov I.P., Vasilyev V.N. – M.: Gos. nauch.-is. in-t inform-kh techn. i telekom-u, 2008. – 46 p.

10. Linda G. Shapiro. Computer Vision / Linda G. Shapiro, George C. Stockman. – New Jersey: Prentice Hall, 2001. – 608 р.

2011_02_gark
2012-11-27  391.08 KB  1739

• < Prev
• Next >