Optimizing of the Number of Spectral Channels in Problems of Processing and Analysis of Hyperspectral Remote Sensing of the Ocean Data
Abstract
The number of modern channels of multispectral and hyperspectral modern optoelectronic systems for remote sensing is redundant for many tasks of the ocean monitoring and requires minimization. The aim of this optimization is a creation of a selection of images obtained from different most informative spectral channels. The selection in dimension is significantly lower than the number of channels of hyperspectral system and provides a solution to the problem of thematic processing. Mathematically, the problem of choosing the most informative spectral channels of hyperspectral survey for pixel of sensor is formulated as the problem of detecting changes in the properties of the registered coordinates or reference discrete spectral image described by a set of spectral components. Set of discrete spectral image components in the analysis is considered as a sequence of independent random Gaussian variables with variance and piecewise constant mean, which abruptly changes from one discrete location to another. The algorithm for solving this problem using methods of statistical estimation as a crucial statistic for detection and maximum likelihood estimation coordinates (spectral channel) changes in the properties of the analyzed process is shown. The consistent application of the algorithm to the sample values of the spectral components allows determination of numbers of the most informative spectral channels. The task of the configuration parameters of the synthesized algorithm to select the most informative spectral channels is considered. The basic parameters are the adjustable threshold value and the size of the sliding window. In accordance with the considered algorithms, implemented in MathLab software programming environment, an example of selecting the most informative spectral channels for spectral image obtained from hyperspectral image pixel of coastal area overgrown with algae is presented.
About the Authors
G. N. MaltsevRussian Federation
Saint-Petersburg
I. A. Kozinov
Russian Federation
Saint-Petersburg
References
1. Kozintsev V. I., Orlov V. M., Belov M. L. Optoelectronic Systems for Environmental Monitoring of the Environment. Moskva, MGTU, 2002. 528 p. (in Russian).
2. Eremeyev V. A., Mordvintsev I. N., Platonov N. G. Modern Hyperspectral Sensors and Processing Methods of Hyperspectral Data. The Study of the Earth From Space. 2003, 6, 80—90 (in Russian).
3. Ryabova N. V., Eskov D. N., Danilkin A. P. Small satellites with optical-electronic equipment in remote sensing programs. Journal of Optical Technology. 1996, 1, 4—19 (in Russian).
4. Maltsev G. N., Kozinov I. A., Danilkin A. P. Space Systems and Technology of Multispectral Remote Sensing of the Earth. Information and Space. 2010, 1, 148—158 (in Russian).
5. Showengerdt R. A. Remote Sensing. Models and Methods for Image Processing. Burlington, Elsever INC, 2007. 560 p. (in Russian).
6. Bondur V. G. Aerospace Methods in Modern Oceanology. New ideas in oceanography. V. 1. Physics. Chemistry. Biology. Moskva, Nauka, 2004, 55—117 (in Russian).
7. Bondur V. G. Complex Satellite Monitoring of Coastal Water Areas. Proc. of 31 Int. Symp. on Remote Sensing of Environment. St.-Petersburg, 2005, 32—35.
8. Grace Chang et al. The new age of hyperspectral oceanography. Oceanography. 2004, 22—29.
9. Tarasov V. V., Yakushenkov Y. G. Multispectral Optoelectronic Systems. Special Equipment. 2002, 4, 56—62 (in Russian).
10. Davis Sh.M. et al. Remote Sensing: the Quantitative Approach. Moskva, Nedra, 1983. 415 p. (in Russian).
11. Maltsev G. N., Kozinov I. A., Fateev V. F. Methods for Selecting the Most Informative Spectral Channels for Remote Sensing of the Earth With Small Spacecraft. Proceedings of the universities. Instrument. 2007, 6, 23—31 (in Russian).
12. Zhigliavskii A.A., Kraskovskii A. E. Detection of the Change-Point in Random Processes in Problems of Radio Engineering. Leningrad, LGU, 1988. 224 p. (in Russian).
13. Nikiforov I. V. Consequent Finding of the Characteristic Change of the Temporary Rows. Moskva, Nauka, 1983. 200 p. (in Russian).
14. Basseville M., Vilski A., Banveniste A. et al. Detection of Abrupt Changes in Signals and Dynamical Systems. New York, Springer-Verlag, 1985. 278 p. (in Russian).
15. Boxing J., Jenkins G. Time Series Analysis: Prognosis and Management. Moskva, Mir, 1974. 408 p. (in Russian).
16. Cramer G. Mathematical Methods of Statistics: lane with English. Moskva, Mir, 1975. 648 p. (in Russian).
17. Kozinov I. A., Maltsev G. N. Modified Algorithm of the Detection of Abrupt Changes in Casual Process and Its Use for Processing of Multispectral Data. Informatsionno-upravlyayushchie sistemy. 2012, 3, 9—17 (in Russian).
Review
For citations:
Maltsev G.N., Kozinov I.A. Optimizing of the Number of Spectral Channels in Problems of Processing and Analysis of Hyperspectral Remote Sensing of the Ocean Data. Fundamental and Applied Hydrophysics. 2015;8(4):92-100. (In Russ.)