V.Yu. Gusev - Post-graduate Student, Moscow Aviation Institute (National Research University). E-mail: gu-sev3@mail.ru

Brightness correction of raw satellite images is a very important issue for the space industry. This processing is performed after re-ceiving and decoding data from the satellite. The distortions caused by non-uniformity of the sensitivity of photo detectors, or faulty settings. This is shown in the image in the form of vertical stripes. This method needs to be robust to work with images with very high natural changes in brightness caused by different types of terrain and clouds. The paper proposes a global correction method based on the minimization of the energy function, defined on the image. Compared to local successive correction methods on the columns of the image, this method is more robust to possible local correction errors and their distribution in the image. Energy function is defined in such way that its minimizing corresponds to the removal of vertical stripes. A special characteristic of the method is the use of energy function with special factors to take into account natural changes in brightness of the image and to prevent unnecessary alignment. Local Otsu segmentation is used for the image analysis method to build energy factors. The methods can be applied at the stage of initial processing of satellite images for automatic correction of vertical stripes. Methods belong to the level of processing 1B according to international classification, which includes radiometric correction and geometric correction of systematic errors of CCD sensors scanning system. Many existing methods for brightness adjustment on the columns are not ideal, and not always able to take into account the strong natural variations in the brightness of the image because of the terrain or clouds. Through the use of global optimization and a special energy function we managed to achieve high and stable correction results in different images.

 

1. Zlobin V.K., Eremeev V.V. Obrabotka aehrokosmicheskikh izobrazhenijj. M.: FIZMATLIT. 2006. 288 s.
2. Barabin G.V., Gusev V.JU., Zajjcev V.E., JUrov N.N. Kombinirovannye metody radiometricheskojj korrekcii kharakternykh artefaktov sputnikovykh izobrazhenijj // Vestnik MAI. 2013. № 3. S. 160−165.
3. Richard Szeliskz Computer vision: Algorithms and Applications. Springer-Verlag London Limited.
4. Osokin A.A. Submoduljarnaja relaksacija v zadache minimizacii ehnergii markovskogo sluchajjnogo polja // Diss. - kand. fiz.‑mat. nauk. M.: 2014. 121 s.
5. Otsu N. A threshold selection method from gray-level histograms // IEEE Trans. Sys. 1979. Man. Cyber. 9: 62−66.
6. Panteleev A.V., Letova T.A. Metody optimizacii v primerakh i zadachakh: Ucheb. posobie. Izd. 2‑e, ispr. M.: Vysshaja shkola. 2005. 544 s.