ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol. 10, No. 2, pp. 284-294

Construction of computational procedure of local image processing on the base of hierarchical regression intended to solve the satellite images processing tasks

V.N. Kopenkov 
Image Processing Systems Institute of RAS
The article is devoted to the use of an algorithm of automatic construction of computational procedure of local nonlinear processing of digital signals/images for satellite images processing. The computational procedure is based on local discrete wavelet transformation of the image used for the preliminary analysis of the image, and hierarchical regression used to obtain the transformation result. The construction procedure is based on training information (pair of images «original» – «the result of processing»), takes into account the restriction on the complexity of the constructed transformation and maximize the processing quality and generalization ability. The paper presents examples of using of the proposed image processing procedure for solving different tasks of satellite images processing.
Keywords: локальная обработка, вейвлет-преобразования, иерархическая регрессия, вычислительная эффективность, космические снимки, local processing, wavelet transform, a hierarchical regression, computational efficiency, space images
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  1. Vorontsov K.V., Matematicheskie voprosy kibernetiki (Mathematical problems of Cybernetics), Moscow: Fizmatlit, 2004, Vol. 13, pp. 5–36.
  2. Kopenkov V.N., Myasnikov V.V., Komp'yuternaya optika, 2012, Vol. 36, No. 2, pp. 257–266.
  3. Гашников М.В., Глумов Н.И., Ильясова Н.Ю., Мясников В.В., Metody komp'yuternoi obrabotki izobrazhenii (Methods of computer processing of images), Moscow: Fizmatlit, 2003, 784 p.
  4. Breiman, L., Friedman J.H., Olshen R.A., Stone C.J., Classification and regression trees, Monterey, Calif., U.S.A.: Wadsworth, Inc. 1984.
  5. Haikin S., Neural Networks: A Comprehensive Foundation, Мoscow: Vilyams, 2006, 1104 p.
  6. Kopenkov V., Effecient algorithms of local discret wavelet transform with HAAR-like bases, Pattern Recognition and Image Analysis, Vol. 18, No. 4, 2008, pp. 654–661.
  7. Kopenkov V.N., Myasnikov V.V., Research the performance of a recursive algorithm of the local discrete wavelet transform, 20-th International Conference on Pattern Recognition (ICPR-2010), Istanbul, Turkey, August 23–26, 2010, Abstract book, 317 p.
  8. Mallat S., A wavelet tour of signal processing, Moscow: Academic Press, 1999, 637 p.