Dr. Aleksandr A. Makarenkov Profile

Dr. Aleksandr A. Makarenkov

at Ryazan State Radio Engineering Univ

SPIE Involvement:

Author

Area of Expertise:

Image processing , Earth remote sensing , Hyperspectral data

Publications (8)

Proceedings Article | 13 September 2021 Presentation + Paper

Experiments on high-detailed mapping of tropospheric NO2 using GSA/Resurs-P observations: results, validation with models and measurements, estimation of emission power

Svetlana Zakharova, Marina Davydova, Alexander Borovski, Karim Shukurov, Yulia Mukhartova, Aleksandr Makarenkov, Oleg Postylyakov

Proceedings Volume 11859, 1185905 (2021) https://doi.org/10.1117/12.2600316

KEYWORDS: Nitrogen dioxide, Data modeling, Pollution, Satellites, Troposphere, Spatial resolution, Atmospheric modeling

Read Abstract +

Proceedings Article | 21 September 2020 Presentation + Paper

On validation high-detail mapping of tropospheric NO2 using GSA/Resurs-P observations with simulated data

Oleg Postylyakov, Alexander Borovski, Karim Shukurov, Yulia Muhartova, Marina Davydova, Aleksandr Makarenkov

Proceedings Volume 11531, 1153109 (2020) https://doi.org/10.1117/12.2574240

KEYWORDS: Nitrogen dioxide, Algorithm development, Spatial resolution, Troposphere, Satellites, Pollution, Reliability

Read Abstract +

Proceedings Article | 18 December 2019 Paper

Comparison of space high-detailed experimental and model data on tropospheric NO2 distribution

Oleg Postylyakov, Alexander Borovski, Nikolay Elansky, Marina Davydova, Svetlana Zakharova, Aleksandr Makarenkov

Proceedings Volume 11208, 112082S (2019) https://doi.org/10.1117/12.2540770

KEYWORDS: Nitrogen dioxide, Pollution, Satellites, Data modeling, Chemical analysis, Spatial resolution, Algorithm development, Error analysis, Hyperspectral imaging, Nitrogen

Read Abstract +

Proceedings Article | 9 October 2019 Presentation + Paper

Preliminary validation of high-detailed GSA/Resurs-P tropospheric NO2 maps with alternative satellite measurements and transport simulations

Oleg Postylyakov, Alexander Borovski, Marina Davydova, Aleksandr Makarenkov

Proceedings Volume 11152, 111520F (2019) https://doi.org/10.1117/12.2535487

KEYWORDS: Nitrogen dioxide, Satellites, Pollution, Algorithm development, Spatial resolution, Error analysis, Troposphere, Hyperspectral imaging

Read Abstract +

Proceedings Article | 7 October 2019 Paper

Clouds segmentation on panchromatic high spatial resolution remote sensing images using convolutional neural networks

V. Eremeev, A. Kuznetcov, A. Kochergin, A. Makarenkov

Proceedings Volume 11155, 111552E (2019) https://doi.org/10.1117/12.2536410

KEYWORDS: Image segmentation, Clouds, Image processing algorithms and systems, Spatial resolution, Image processing, Convolution, Remote sensing, Convolutional neural networks, Satellite imaging, Satellites

Read Abstract +

Proceedings Article | 9 October 2018 Paper

Modular transfer function compensation for hyperspectral data from Resurs-P satellite system

Aleksandr Makarenkov, Nikolay Egoshkin, Victor Eremeev

Proceedings Volume 10789, 107891L (2018) https://doi.org/10.1117/12.2325531

KEYWORDS: Point spread functions, Image filtering, Modulation transfer functions, Image resolution, Hyperspectral imaging, Satellites, Signal to noise ratio, Spatial resolution, Filtering (signal processing)

Read Abstract +

Resurs-P satellite system is one of the recent Earth remote sensing systems deployed by Russia. Its payload consists of the high resolution multispectral imager, the average resolution imager with wide swath and the hyperspectral imaging system. Hyperspectral system consists of two imagers each registering radiation in roughly half of instruments spectral range. So the output from the hyperspectral system are two hyperspectral images representing same area of the Earth but in different spectral ranges with a slight spectral overlap. For further explanation purposes these two images are named as image ‘A’ and image ‘B’. During the on-ground processing stage images ‘A’ and ‘B’ are combined into a single hyperspectral image, covering whole instrument spectral range. During evaluation of quality of hyperspectral data it was found that modular transfer function (MTF) obtained from images ‘A’ and ‘B’ is different, resulting in better spatial resolution of image ‘A’ compared to ‘B’. This fact could pose problems in the following analysis of hyperspectral data as the obtained spectral signatures actually represent slightly different parts of the ground in two halves of an instrument spectral range. The present work describes an algorithm of MTF compensation which purpose is to mitigate difference in spatial resolution of the data, obtained from the hyperspectral imaging system of Resurs-P satellite. The proposed algorithm is based on spatial linear filtering and is applied on the data that was previously transformed to spectral radiances and spatially co-registered. The algorithm consists of two steps. On the first step the coefficients of correction linear filter defined as a window kernel are estimated. For filter estimation we choose one spectral band from image ‘A’ as a reference image with the ‘best’ MTF and one spectral band from image ‘B’. We select spectral bands from within spectral overlap range of images ‘A’ and ‘B’ so they have same spectral ranges. Then linear filter coefficients are calculated using the least square errors method, so that when applying calculated filter to image ‘B’ an image that is closest to ‘A’ is obtained. On the second step correction filter is applied to all bands in image ‘B’ to compensate its difference in MTF compared to image ‘A’. Based on the selection of reference image it is possible to estimate the correction filter that blurs higher resolution image to lower resolution (which also reduces noise) or vice versa, i.e. the filter that increases resolution (but at the cost of increased noise). Effectiveness of the proposed algorithm is evaluated on the images obtained from Resurs-P satellites. The relative difference of resolutions of ‘A’ and ‘B’ images is reduced by more than 3 times.

Proceedings Article | 9 October 2018 Paper

Segmentation of low-informative areas on panchromatic high spatial resolution images of Earth surface

V. Eremeev, A. Kuznetcov, A. Kochergin, A. Makarenkov, N. Egoshkin

Proceedings Volume 10789, 107891F (2018) https://doi.org/10.1117/12.2325095

KEYWORDS: Image segmentation, Image processing algorithms and systems, Image processing, Spatial resolution, Algorithm development, Sun, Solar radiation, Earth's atmosphere, Reflectivity, Clouds

Read Abstract +

Proceedings Article | 30 November 2017 Paper

First experiment on retrieval of tropospheric NO2 over polluted areas with 2.4-km spatial resolution basing on satellite spectral measurements

Oleg Postylyakov, Alexander Borovski, Aleksandr Makarenkov

Proceedings Volume 10466, 104662Y (2017) https://doi.org/10.1117/12.2285794

KEYWORDS: NOx, Satellites, Spatial resolution, Pollution, Spectral resolution, Hyperspectral imaging, Error analysis, Remote sensing

Read Abstract +

Showing 5 of 8 publications

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years