RADIOELECTRONIC AND COMPUTER SYSTEMS

The subject matter of the article is the methods of morphological spatial filtering of images in pseudo-colors obtained as a result of statistical segmentation of multichannel satellite images. The aim is to study the effectiveness of various methods of post-classification image processing in order to increase the probability of correct recognition for observed objects. The tasks to be solved are: to select a mathematical model describing the training sets of objects’ classes; to implement the procedure of statistical controlled classification by the maximum likelihood method; to evaluate the results of objects’ recognition on the test image by the criterion of the empirical probability of correct recognition; to formalize the procedures of local object-oriented filtering of a segmented image; to investigate the effectiveness of rank filtering as well as weighted median filtering procedures taking into account the results of the classification by k-nearest neighbors in the filter window. The methods used are methods of empirical distributions’ approximation, statistical recognition methods, methods of probability theory and mathematical statistics, methods of local spatial filtering. The following results were obtained. A method for synthesizing a universal mathematical model has been proposed for describing non-Gaussian signal characteristics of objects on multichannel images based on a multi-dimensional variant of Johnson SB distribution; this model was used for statistical pixel-by-pixel classification of the original satellite image. Algorithms for local post-classification processing in the neighborhood of the selected segments boundaries have been implemented. The analysis of the developed algorithms’ effectiveness based on estimates of classes’ correct recognition probability is performed. Conclusions. The scientific novelty of the results obtained is as follows: combined approaches to the pattern recognition procedures have been further developed – it has been shown that the use of methods of local object-oriented filtering of segmented images allows to reduce the number of point errors for element-wise classification of spatial objects.

Bishop, Ch. M. Pattern Recognition and Machine Learning. Springer, 2010. 738 p.

Schowengerdt, R. Remote Sensing: Models and Methods for Image Processing. 3rd ed. Cambridge : Academic Press, 2006. 560 p. doi: 10.978.00804/80589.

Niemistö, A., Shmulevich, I., Lukin, V., Dolia, A., Yli-Harja, O. Correction of Misclassifications Using a Proximity-Based Estimation Method. EURASIP J. Adv. Sig. Proc., 2004, pp. 1142 – 1155. doi: 10.1155/S1110865704402145.

Vasil'eva, I. K., Pankratova, E. A. Morfologicheskaya obrabotka izobrazhenii v psevdotsvetakh kak sposob povysheniya pravdopodobnosti klassifikatsii [Morphological image processing in pseudo-colors as a way to increase the likelihood of classification]. Radioelektronni i komp’juterni systemy – Radioelectronic and computer systems, 2011, no. 3 (51), pp. 64 – 67. (In Russian).

Popov, A. V. Metod prinyatiya reshenii pri raspoznavanii ob"ektov v usloviyakh sushchestvennoi apriornoi neopredelennosti [A Decision-Making Method at Recognizing Objects in Conditions of Essential Prior Uncertainty]. Radioelektronni i komp’juterni systemy – Radioelectronic and computer systems, 2015, no. 3, pp. 53 – 60. (In Russian).

Skakun, S., Kussul, N., Shelestov, A., Lavreniuk, M., Kussul, O. Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 2016, vol. 9(8), pp. 3712 – 3719. doi: 10.1109/JSTARS.2015.2454297.

Lukin, V., Abramov, S., Krivenko, S., Kurekin, A., Pogrebnyak, O. Analysis of classification accuracy for pre-filtered multichannel remote sensing data. Expert Systems with Applications, 2013, vol. 40, pp. 6400 – 6411.

Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 2017, vol. 14(5), pp. 778 – 782. doi: 10.1109/LGRS.2017.2681128.

Khan, G., Shapiro, S. Statisticheskie modeli v inzhenernykh zadachakh [Statistical models in engineering problems]. Moscow, Mir Publ., 1969. 369 p.

Babakov, M. F. Ob odnom sposobe approksimatsii raspredeleniy mnogomernykh polyarimetricheskikh kharakteristik [About one method of multidimensional polarimetric characteristics distributions approximation]. Trudy KhAI «Avtomatizirovannye sistemy upravleniya» – Proc. of the KhAI “Automated control systems”, 1981, no. 3, pp. 166–167. (In Russian).

Congalton, R. Accuracy assessment and validation of remotely sensed and other spatial information. International Journal of Wildland Fire, 2001, vol. 10, pp. 321 – 328. doi: 10.1071/WF01031.

Gurchenkov, A. A., Murynin, A. B., Trekin, A. N., Ignat'ev V. Yu. Metod ob"ektno-orientirovannoi klassifikatsii ob"ektov podstilayushchei poverkhnosti v zadache aerokosmicheskogo monitoringa sostoyaniya impaktnykh raionov Arktiki [Object-Oriented Classification of Substrate Surface Objects in Arctic Impact Regions Aerospace Monitoring]. Vestnik MGTU im. N. E. Baumana. Seriya Estestvennye nauki – Herald of the Bauman MSTU. Series Natural Sciences, 2017, no. 3, pp. 135 – 146. (In Russian). doi: 10.18698/1812-3368-2017-3-135-146.

Vasil'eva, I., Popov, А. Multicomponent Model of Objects Attributive Signatures on Color Pictures. Proc. Internat. Scientific-Practical Conf. on Problems of Infocommunications. Science and Technology, Kharkiv, Ukraine, 9-12 Oct. 2018, pp. 281 – 284. doi: 10.1109/INFOCOMMST.2018.8632110.

Abramov, N. S., Makarov, D. A., Talalaev, A. A., Fralenko, V. P. Sovremennye metody intellektual'noi obrabotki dannykh DZZ [Modern methods for intelligent processing of Earth remote sensing data]. Programmnye sistemy : teoriya i prilozheniya – Program Systems : Theory and Applications, 2018, vol. 9, no. 4(39), pp. 417 – 442. (In Russian). doi: 10.25209/2079-3316-2018-9-4-417-442.

Afanas'ev, A. A., Zamyatin A. V. Gibridnye metody avtomatizirovannoi identifikatsii izmenenii landshaftnogo pokrova po dannym distantsionnogo zondirovaniya Zemli v usloviyakh shumov [Hybrid methods of automated identification of changes in landscape cover by Earth remote sensing data in the conditions of noises]. Komp'yuternaya optika – Computer optics, 2017, vol. 41, no. 3, pp. 431 – 440. (In Russian). doi: 10.18287/2412-6179-2017-41-3-431-440.