<div>
	SEA SURFACE IMAGE SUB-BAND ANALYSIS BASED ON COSINE TRANSFORM</div>

Daria A. Chernomorets, Mikhelev Vladimir M, Evgeniya Bolgova V, Andrey A. Chernomorets

2019 , VOLUME 19, NUMBER 6 ( november-december )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

Publications

Editor-in-Chief

Nikiforov
Vladimir O.
D.Sc., Prof.

Partners

doi: 10.17586/2226-1494-2019-19-6-1072-1078

SEA SURFACE IMAGE SUB-BAND ANALYSIS BASED ON COSINE TRANSFORM

D. A. Chernomorets, V. M. Mikhelev, E. V. Bolgova, A. A. Chernomorets

Read the full article

Article in Russian

For citation:

Chernomorets D.A., Mikhelev V.M., Bolgova E.V., Chernomorets A.A. Sea surface image sub-band analysis based on cosine transform. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 6, pp. 1072–1078 (in Russian). doi: 10.17586/2226-1494-2019-19-6-1072-1078

Abstract

Subject of Research. We propose an approach to the research of the frequency properties of the sea surface images sequence (video frames) on the basis of sub-band analysis within the cosine transform. The study of frequency properties makes it possible to solve the problem of object detection on the agitated sea surface. Method. The main theoretical propositions of the sub-band analysis within the cosine transform are given. The paper presents the method of formation of the cosine transform sub-band matrices used in data analysis and method of calculation of the analyzed vector energy parts in different frequency intervals. The sub-band approach to the analysis of the image component energy distribution in the frequency domain is described. Realization of separate components of the sea surface image sequence (video frames) in the vector form is shown. The vectors contain the pixel values with the same coordinates in the image set and give the possibility to calculate the energy parts in specified frequency intervals. Main Results. Video of the sea surface with various agitation levels were used in computing experiments. The distribution analysis of the energy part values of the matching components in the used video records showed that different sea surface agitation corresponds to different distributions of the matching component energy in the frequency domain. It has been found that at low agitation, the energy of the image components is concentrated in the low-frequency region and with the increase of the sea surface agitation, the energy part values of the corresponding image components increase in the high-frequency region. Practical Relevance. The obtained distribution features of the sea surface image component energy parts can be used when solving the detection problems for objects, floating on the sea surface, based on the analysis of their frequency characteristics.

Keywords: agitated sea surface, sub-band analysis, cosine transform, sub-band matrix, energy part, frequency interval

Acknowledgements. The reported study was funded by the RFBR, project number 19-07-00657.

References

Kravchenko V.F., Lutsenko V.I., Lutsenko I.V.. Scattering of radio waves by the sea and detection of objects against its background. Moscow, FIZMATLIT Publ., 2015, 448 p. (in Russian)
Rodriguez-Blanco M., Golikov V. Multiframe GLRT-based adaptive detection of multipixel targets on a sea surface. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, vol. 9, no. 12, pp. 5506–5512. doi: 10.1109/JSTARS.2016.2582383
Golikov V., Rodriguez-Blanco M., Lebedeva O. Robust multipixel matched subspace detection with signal-dependent background power. Journal of Applied Remote Sensing, 2016, vol. 10, no. 1, pp. 015006. doi: 10.1117/1.JRS.10.015006
Manolakis D., Truslow E., Pieper M., Cooley T., Brueggeman M. Detection algorithms in hyperspectral imaging systems: an overview of practical algorithms. IEEE Signal Processing Magazine, 2014, vol. 31, no. 1, pp. 24–33. doi: 10.1109/MSP.2013.2278915
Scharf L.L., Friedlander B. Matched subspace detectors. IEEE Transactions on Signal Processing, 1994, vol. 42, no. 8, pp. 2146–2157. doi: 10.1109/78.301849
Harsanyi J.C., Chang C.-I. Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Transactions on Geoscience and Remote Sensing, 1994, vol. 32, no. 4, pp. 779–785. doi: 10.1109/36.298007
Kraut S., Scharf L.L., McWhorter L.T. Adaptive subspace detectors. IEEE Transactions on Signal Processing, 2001, vol. 49, no. 1, pp. 1–16. doi: 10.1109/78.890324
Chen Y., Nasrabadi N.M., Tran T.D. Sparse representation for target detection in hyperspectral imagery. IEEE Journal on Selected Topics in Signal Processing, 2011, vol. 5, no. 3, pp. 629–640. doi: 10.1109/JSTSP.2011.2113170
Manolakis D., Siracusa C., Shaw G. Hyperspectral subpixel target detection using the linear mixing model. IEEE Transactions on Geoscience and Remote Sensing, 2001, vol. 39, no. 7, pp. 1392–1409. doi: 10.1109/36.934072
Zhang L., Zhang L., Tao D., Huang X., Du B. Hyperspectral remote sensing image subpixel target detection based on supervised metric learning. IEEE Transactions on Geoscience and Remote Sensing, 2014, vol. 52, no. 8, pp. 4955–4965. doi: 10.1109/TGRS.2013.2286195
Golikov V., Lebedeva O., Rodriguez-Blanco M. GLRT subspace detection of multi-pixel targets with known and unknown spatial parameters in presence of signal-dependent background power. Computers and Electrical Engineering, 2016, vol. 52, pp. 38–48. doi: 10.1016/j.compeleceng.2016.04.016
Zhilyakov E.G., Chernomorets A.A. Optimal separation of image subband components. Information Systems and Technologies, 2013, no. 1(75), pp. 5–11. (in Russian)
Zhilyakov E.G., Belov S.P., Chernomorets A.A., Rakov V.I. Variational methods of analysis of signals based on the frequency of ideas. International Journal of Computer Applications in Technology, 2016, vol. 9, no. 35, pp. 71–76.
Chernomorets A.A., Bolgova E.V. On the analysis of data based on the cosine transformation. Belgorod State University Scientific Bulletin. Economics. Information technologies, 2015, no. 1(198), pp. 68–73. (in Russian)
Chernomorets A.A., Bolgova E.V., Chernomorets D.A. The generalized subband analysis on the basis of unitary transformations. Belgorod State University Scientific Bulletin. Economics. Information technologies, 2015, no. 7(204), pp. 97–104. (in Russian)
Bolgova E.V. The properties of subinterval matrices for a two-dimensional cosine transform. Information Systems and Technologies, 2017, no. 6(104), pp. 1928. (in Russian)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License