Ирина Карловна Васильева, Владимир Васильевич Лукин


The subject matter of the article are the methods of local spatial post-processing of images obtained as a result of statistical per-pixel classification of multichannel satellite images distorted by additive Gaussian noise. The aim is to investigate the effectiveness of some variants of post-classification image processing methods over a wide range of signal-to-noise ratio; as a criterion of effectiveness, observed objects classification reliability indicators have been taken. The tasks to be solved are: to generate random values of the noise components brightness, ensuring that they coincide with the adopted probabilistic model; to implement a procedure of statistical controlled classification by the maximum likelihood method for images distorted by noise; to evaluate the results of the objects selection in noisy images by the criterion of the empirical probability of correct recognition; to implement procedures for local object-oriented post-processing of images; to investigate the effect of noise variance on the effectiveness of post-processing procedures. The methods used are: methods of stochastic simulation, methods of approximation of empirical dependencies, statistical methods of recognition, methods of probability theory and mathematical statistics, methods of local spatial filtering. The following results have been obtained. Algorithms of rank and weighted median post-processing with considering the results of classification by k-nearest neighbors in the filter window were implemented. The developed algorithms efficiency analysis that based on estimates of the correct recognition probability for objects on noisy images was carried out. Empirical dependences of the estimates of the overall recognition errors probability versus the additive noise variance were obtained. Conclusions. The scientific novelty of the results obtained is as follows: combined approaches to building decision rules, taking into account destabilizing factors, have been further developed – it has been shown that the use of methods of local object-oriented filtering of segmented images reduces the number of point errors in the element-based classification of objects, as well as partially restores the connectedness and spatial distribution of image structure elements.


classification; approximation; additive Gaussian noise; spatial filtering; probability of correct recognition


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.

Lukin, V., Ponomarenko, N., Egiazarian, K., Astola, J. Adaptive DCT-based filtering of images corrupted by spatially correlated noise. Proc. SPIE Conference Image Processing: Algorithms and Systems VI, 2008, vol. 6812. 12 p.

Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K. Image denoising by sparse 3D transform-domain collaborative filtering. J. IEEE Transactions on Image Processing, 2007, vol. 16(8), pp. 2080 – 2095.

Zhong, P., Wang, R. Multiple-Spectral-Band CRFs for Denoising Junk Bands of Hyperspectral Imagery. IEEE Transactions on Geoscience and Remote Sensing, 2013, vol. 51(4), pp. 2269 – 2275.

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.

Yuan, Q., Zhang, L., Shen, H. Hyperspectral Image Denoising With a Spatial–Spectral View Fusion Strategy. IEEE Trans. Geosci. Remote Sens., 2014, vol. 52, pp. 2314 – 2325.

Uss, M., Vozel, B., Lukin, V., Abramov, S., Baryshev, I., Chehdi, K. Image Informative Maps for Estimating Noise Standard Deviation and Texture Parameters. EURASIP Journal on Advances in Signal Processing, 2011, vol. 2011, 12 p. doi:10.1155/2011 /806516.

Meola, J., Eismann, M. T., Moses, R. L., Ash, J. N. Modeling and estimation of signal-dependent noise in hyperspectral imagery. J. Appl. Opt., 2011, vol. 50, pp. 3829 – 3846.

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.

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.

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.

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.

Vasil'eva, I. K., Lukin, V. V. Analiz metodov postklassifikatsionnoi obrabotki mnogokanal'nykh izobrazhenii [Multichannel images post-classification processing techniques analysis]. Radioelektronni i komp’juterni systemy – Radioelectronic and computer systems, 2019, no. 1 (89), pp. 17 – 28. (In Russian). doi: 10.32620/reks.2019.1.00.

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.

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).

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.

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.



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