Vladimir Lukin

By Sergey Abramov, Victoria Zabrodina, Vladimir Lukin, Benoit Vozel, Kacem Chehdi and Jaakko Astola

Part of the book: Numerical Analysis

By Vladimir Lukin, Nikolay Ponomarenko, Dmitriy Fevralev, Benoit Vozel, Kacem Chehdi and Andriy Kurekin

Part of the book: Remote Sensing

By Sergey Abramov, Victoriya Abramova, Vladimir Lukin, Nikolay Ponomarenko, Benoit Vozel, Kacem Chehdi, Karen Egiazarian and Jaakko Astola

Part of the book: Computational and Numerical Simulations

By Benoit Vozel, Oleksiy Rubel, Alexander Zemliachenko, Sergey Abramov, Sergey Krivenko, Ruslan Kozhemiakin, Vladimir Lukin and Kacem Chehdi

In processing of multichannel remote sensing data, there is a need in automation of basic operations as filtering and compression. Automation presumes undertaking a decision on expedience of image filtering. Automation also deals with obtaining of information based on which certain decisions can be undertaken or parameters of processing algorithms can be chosen. For the considered operations of denoising and lossy compression, it is shown that their basic performance characteristics can be quite easily predicted based on easily calculated local statistics in discrete cosine transform (DCT) domain. The described methodology of prediction is shown to be general and applicable to different types of noise under condition that its basic characteristics are known in advance or pre-estimated accurately.

Part of the book: Environmental Applications of Remote Sensing

By Vladimir Lukin, Alexander Zemliachenko, Ruslan Kozhemiakin, Sergey Abramov, Mikhail Uss, Victoriya Abramova, Nikolay Ponomarenko, Benoit Vozel and Kacem Chehdi

In this chapter, we consider lossy compression of multichannel images acquired by remote sensing systems. Two main features of such data are taken into account. First, images contain inherent noise that can be of different intensity and type. Second, there can be essential correlation between component images. These features can be exploited in 3D compression that is demonstrated to be more efficient than component-wise compression. The benefits are in considerably higher compression ratio attained for the same or even less distortions introduced. It is shown that important performance parameters of lossy compression can be rather easily and accurately predicted.

Part of the book: Recent Advances in Image and Video Coding

By Vladimir Lukin, Oleksii Rubel, Ruslan Kozhemiakin, Sergey Abramov, Andrii Shelestov, Mykola Lavreniuk, Mykola Meretsky, Benoit Vozel and Kacem Chehdi

This chapter addresses an important practical task of classification of multichannel remote sensing data with application to multitemporal dual-polarization Sentinel radar images acquired for agricultural regions in Ukraine. We first consider characteristics of dual-polarization Sentinel radar images and discuss what kind of filters can be applied to such data. Several examples of denoising are presented with analysis of what properties of filters are desired and what can be provided in practice. It is also demonstrated that the use of preliminary denoising produces improvement of classification accuracy where despeckling that is more efficient in terms of standard filtering criteria results in better classification.

Part of the book: Recent Advances and Applications in Remote Sensing

By Vladimir Lukin, Alexander Zemliachenko, Sergey Krivenko, Benoit Vozel and Kacem Chehdi

In this chapter, approaches to provide a desired quality of remote sensing images compressed in a lossy manner are considered. It is shown that, under certain conditions, this can be done automatically and quickly using prediction of coder performance parameters. The main parameters (metrics) are mean square error (MSE) or peak signal-to-noise ratio (PSNR) of introduced losses (distortions) although prediction of other important metrics is also possible. Having such a prediction, it becomes possible to set a quantization step of a coder in a proper manner to provide distortions of a desired level or less without compression/decompression iterations for single-channel image. It is shown that this approach can be also exploited in three-dimensional (3D) compression of multichannel images to produce a larger compression ratio (CR) for the same or less introduced distortions as for component-wise compression of multichannel data. The proposed methods are verified for test and real life images.

Part of the book: Satellite Information Classification and Interpretation