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$12 USD / hour
Flag of UKRAINE
kharkiv, ukraine
$12 USD / hour
It's currently 11:25 AM here
Joined January 20, 2016
0 Recommendations

svch1mail

@svch1mail

 5.0 (1 review)
3.0
3.0
100%
100%
$12 USD / hour
Flag of UKRAINE
kharkiv, ukraine
$12 USD / hour
100%
Jobs Completed
100%
On Budget
100%
On Time
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Repeat Hire Rate

Scientific Researcher

PhD, Ass. prof., expert in Earth Observation Sensors and image processing > 15 years. Experience in satellite image calibration (Sich-2; Landsat-4,5,7,8; EO-1; Hyperion, Sentinel-1, 2) on Matlab and Sentinel SNAP and Google Earth Engine. Developed algorithms and software for in-flight radiometric calibration and ground resolution estimation in 4 contracts with SSAU, subpixel satellite image processing, data merging from different satellites (S1, S2, LC8), made multiband visual-thermal-radar satellite images products. Developed GUI Matlab software for image and sound watermarking, steganography and image forgery detection. Have > 22 years experience in the mathematical simulations of different problems (statistics, linear algebra, differential and integral equations, nonlinear algebraic equations, approximations) with Python, JavaScript, Matlab, Exel, FEMM, LTSpice. Machine learnig on Matlab, Pyhon. Was the scientific adviser of 15 successfully adopted PhD thesis.
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Portfolio

2985182
Main goal of my client in this project was automatical vessel lumen recognition on the TVC/IVUS DICOM images. I developed multifunctional Matlab-based software for automatically lumen recognition on the DICOM images series with parallel images processing, compensation of the different image artefacts and multilevel vessel lumen contour corrections. As a results of processing there are video file with vessel lumen contour highlighted (green), files with vessel lumen border coordinates, files with information contour quality estimation flags. Software have parallel processing, debug and self tuning modes. Also included functions of contor correction and new contour drawing.
Vessel Lumen recognition on TVC/IVUS DICOM images
2733824
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
2710800
In this project photogrammetric technology for estimation heghts of the ground objects by Google Earth images has tested. Technology gives possibility to estimate height of object by single image if in this image exist element with known size.
Photogrammetric measurements
In this project photogrammetric technology for estimation heghts of the ground objects by Google Earth images has tested. Technology gives possibility to estimate height of object by single image if in this image exist element with known size.
Photogrammetric measurements
In this project photogrammetric technology for estimation heghts of the ground objects by Google Earth images has tested. Technology gives possibility to estimate height of object by single image if in this image exist element with known size.
Photogrammetric measurements
2679280
Project focused on making multimission product, based on merging different spectral layers from the different satellites. As example shown pseudocolor image where Landsat-8 panchromatic band 8(red), Sentinel-1 channel VH(green) and Sentinel-1 channel VV (blue) . Image is of 15m resolution, as Landsat-8 band 8 layer was as master. Have been succsessfully tested possibility to combine Sentinel-2 and Sentinel-1. In this S2-S1 combined product to S2 bands (B1-B12) was added new band S1 VH. So, if necessary, S1,S2, LC8 can be combined in one output product. Such combinations can was done by Sentinel SNAP Tools.
Satellite images merging
2562705
In this project Matlab software for optimization ad-hoc frequency hopping telecommunication systems has developed. For adaptation to channels noises applyed Particle Swarm Optimization algorithm. Adaptation occurs through the search interference-free channels, or channels with lower noise. Proposed approach use only internal channel information and not require additional monitoring information.
Ad-Hoc systems PSO optimization
2562681
In this project Matlab GUI software for deconvolution enhancement of blurred images with application of algorithms of Particle Swarm Optimization has developed.
Deconvolution Processing of the Images
In this project Matlab GUI software for deconvolution enhancement of blurred images with application of algorithms of Particle Swarm Optimization has developed.
Deconvolution Processing of the Images
2985182
Main goal of my client in this project was automatical vessel lumen recognition on the TVC/IVUS DICOM images. I developed multifunctional Matlab-based software for automatically lumen recognition on the DICOM images series with parallel images processing, compensation of the different image artefacts and multilevel vessel lumen contour corrections. As a results of processing there are video file with vessel lumen contour highlighted (green), files with vessel lumen border coordinates, files with information contour quality estimation flags. Software have parallel processing, debug and self tuning modes. Also included functions of contor correction and new contour drawing.
Vessel Lumen recognition on TVC/IVUS DICOM images
2733824
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
This project focused on the Land cover unsupervised classification. Used K-means classification method. Research was done on two ways. First is change 10, 30 and 50 classes for B1-B12 Sentinel-2 channels. The second way is the Land cover classificaation, based on NDVI, fAPAR, LAI and Ch index calculation and use only B4-B5, B4-B6, B4-B7 and B4-B8a Sentinel-2 channels. For K-means classification and calculations mentioned vegetation indexes Jython (Java-Python) two plug-ins for Sentinel SNAP has been developed.
Land cover classification
2710800
In this project photogrammetric technology for estimation heghts of the ground objects by Google Earth images has tested. Technology gives possibility to estimate height of object by single image if in this image exist element with known size.
Photogrammetric measurements
In this project photogrammetric technology for estimation heghts of the ground objects by Google Earth images has tested. Technology gives possibility to estimate height of object by single image if in this image exist element with known size.
Photogrammetric measurements
In this project photogrammetric technology for estimation heghts of the ground objects by Google Earth images has tested. Technology gives possibility to estimate height of object by single image if in this image exist element with known size.
Photogrammetric measurements
2679280
Project focused on making multimission product, based on merging different spectral layers from the different satellites. As example shown pseudocolor image where Landsat-8 panchromatic band 8(red), Sentinel-1 channel VH(green) and Sentinel-1 channel VV (blue) . Image is of 15m resolution, as Landsat-8 band 8 layer was as master. Have been succsessfully tested possibility to combine Sentinel-2 and Sentinel-1. In this S2-S1 combined product to S2 bands (B1-B12) was added new band S1 VH. So, if necessary, S1,S2, LC8 can be combined in one output product. Such combinations can was done by Sentinel SNAP Tools.
Satellite images merging
2562705
In this project Matlab software for optimization ad-hoc frequency hopping telecommunication systems has developed. For adaptation to channels noises applyed Particle Swarm Optimization algorithm. Adaptation occurs through the search interference-free channels, or channels with lower noise. Proposed approach use only internal channel information and not require additional monitoring information.
Ad-Hoc systems PSO optimization
2562681
In this project Matlab GUI software for deconvolution enhancement of blurred images with application of algorithms of Particle Swarm Optimization has developed.
Deconvolution Processing of the Images
In this project Matlab GUI software for deconvolution enhancement of blurred images with application of algorithms of Particle Swarm Optimization has developed.
Deconvolution Processing of the Images

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5.0
$300.00 USD
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High quality work with good replying on time. I will definitely use him again!
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Flag of Lep E.
@Muna2014
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8 years ago

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