Dr. Valentyn A. Tolpekin Profile

Dr. Valentyn A. Tolpekin

at Univ Twente

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Author

Publications (7)

Proceedings Article | 18 October 2016 Presentation + Paper

Tree detection in orchards from VHR satellite images using scale-space theory

Milad Mahour, Valentyn Tolpekin, Alfred Stein

Proceedings Volume 10004, 100040B (2016) https://doi.org/10.1117/12.2241529

KEYWORDS: Agriculture, Satellite imaging, Satellites, Earth observing sensors, Spatial resolution, Remote sensing, Blob detection, Sun, Sensors, Image fusion

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Proceedings Article | 5 May 2016 Paper

Semi-empirical modelling for forest above ground biomass estimation using hybrid and fully PolSAR data

Kiledar Tomar, Shashi Kumar, Valentyn Tolpekin, Sushil Joshi

Proceedings Volume 9877, 987729 (2016) https://doi.org/10.1117/12.2223639

KEYWORDS: Data modeling, Scattering, Modeling, Carbon, Synthetic aperture radar, Vegetation, Electromagnetic scattering, RGB color model, Polarization, Polarimetry, Clouds

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Forests act as sink of carbon and as a result maintains carbon cycle in atmosphere. Deforestation leads to imbalance in global carbon cycle and changes in climate. Hence estimation of forest biophysical parameter like biomass becomes a necessity. PolSAR has the ability to discriminate the share of scattering element like surface, double bounce and volume scattering in a single SAR resolution cell. Studies have shown that volume scattering is a significant parameter for forest biophysical characterization which mainly occurred from vegetation due to randomly oriented structures. This random orientation of forest structure causes shift in orientation angle of polarization ellipse which ultimately disturbs the radar signature and shows overestimation of volume scattering and underestimation of double bounce scattering after decomposition of fully PolSAR data. Hybrid polarimetry has the advantage of zero POA shift due to rotational symmetry followed by the circular transmission of electromagnetic waves. The prime objective of this study was to extract the potential of Hybrid PolSAR and fully PolSAR data for AGB estimation using Extended Water Cloud model. Validation was performed using field biomass. The study site chosen was Barkot Forest, Uttarakhand, India. To obtain the decomposition components, m-alpha and Yamaguchi decomposition modelling for Hybrid and fully PolSAR data were implied respectively. The RGB composite image for both the decomposition techniques has generated. The contribution of all scattering from each plot for m-alpha and Yamaguchi decomposition modelling were extracted. The R2 value for modelled AGB and field biomass from Hybrid PolSAR and fully PolSAR data were found 0.5127 and 0.4625 respectively. The RMSE for Hybrid and fully PolSAR between modelled AGB and field biomass were 63.156 (t ha-1) and 73.424 (t ha-1) respectively. On the basis of RMSE and R2 value, this study suggests Hybrid PolSAR decomposition modelling to retrieve scattering element for AGB estimation from forest.

Proceedings Article | 5 May 2016 Paper

Polarimetric SAR interferometry-based decomposition modelling for reliable scattering retrieval

Neeraj Agrawal, Shashi Kumar, Valentyn Tolpekin

Proceedings Volume 9877, 987708 (2016) https://doi.org/10.1117/12.2223977

KEYWORDS: Scattering, Synthetic aperture radar, Polarimetry, Interferometry, Modeling, Biological research, RGB color model, Remote sensing, Data modeling, Electromagnetism

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Fully Polarimetric SAR (PolSAR) data is used for scattering information retrieval from single SAR resolution cell. Single SAR resolution cell may contain contribution from more than one scattering objects. Hence, single or dual polarized data does not provide all the possible scattering information. So, to overcome this problem fully Polarimetric data is used. It was observed in previous study that fully Polarimetric data of different dates provide different scattering values for same object and coefficient of determination obtained from linear regression between volume scattering and aboveground biomass (AGB) shows different values for the SAR dataset of different dates. Scattering values are important input elements for modelling of forest aboveground biomass. In this research work an approach is proposed to get reliable scattering from interferometric pair of fully Polarimetric RADARSAT-2 data. The field survey for data collection was carried out for Barkot forest during November 10th to December 5th, 2014. Stratified random sampling was used to collect field data for circumference at breast height (CBH) and tree height measurement. Field-measured AGB was compared with the volume scattering elements obtained from decomposition modelling of individual PolSAR images and PolInSAR coherency matrix. Yamaguchi 4-component decomposition was implemented to retrieve scattering elements from SAR data. PolInSAR based decomposition was the great challenge in this work and it was implemented with certain assumptions to create Hermitian coherency matrix with co-registered polarimetric interferometric pair of SAR data. Regression analysis between field-measured AGB and volume scattering element obtained from PolInSAR data showed highest (0.589) coefficient of determination. The same regression with volume scattering elements of individual SAR images showed 0.49 and 0.50 coefficients of determination for master and slave images respectively. This study recommends use of interferometric PolSAR data for reliable scattering retrieval.

SPIE Journal Paper | 23 February 2016

Forest encroachment mapping in Baratang Island, India, using maximum likelihood and support vector machine classifiers

Laxmi Tiwari, Satish Sinha, Sameer Saran, Valentyn Tolpekin, Penumetcha Raju

JARS, Vol. 10, Issue 01, 016016, (February 2016) https://doi.org/10.1117/12.10.1117/1.JRS.10.016016

KEYWORDS: Spatial resolution, Remote sensing, Image classification, Geographic information systems, Statistical analysis, Global Positioning System, Visualization, Accuracy assessment, Satellites, Sensors

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SPIE Journal Paper | 27 February 2014

Detection of built-up area in optical and synthetic aperture radar images using conditional random fields

Benson Kenduiywo, Valentyn Tolpekin, Alfred Stein

JARS, Vol. 8, Issue 01, 083672, (February 2014) https://doi.org/10.1117/12.10.1117/1.JRS.8.083672

KEYWORDS: Synthetic aperture radar, Data modeling, Magnetorheological finishing, Satellites, Image classification, Speckle, Spatial resolution, Satellite imaging, Earth observing sensors, Feature selection

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SPIE Journal Paper | 13 March 2013

Anisotropic kriging to derive missing coseismic displacement values obtained from synthetic aperture radar images

Muhammad Yaseen, Nicholas Hamm, Valentyn Tolpekin, Alfred Stein

JARS, Vol. 7, Issue 01, 073580, (March 2013) https://doi.org/10.1117/12.10.1117/1.JRS.7.073580

KEYWORDS: Anisotropy, Earthquakes, Synthetic aperture radar, Tolerancing, Spherical lenses, Interferometric synthetic aperture radar, Adaptive optics, Signal to noise ratio, Buildings, Global Positioning System

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Proceedings Article | 8 November 2012 Paper

Detection of built-up area expansion in ASTER and SAR images using conditional random fields

Benson Kipkemboi Kenduiywo, Valentyn Tolpekin, Alfred Stein

Proceedings Volume 8537, 85370N (2012) https://doi.org/10.1117/12.965886

KEYWORDS: Data modeling, Synthetic aperture radar, Magnetorheological finishing, Speckle, Feature extraction, Feature selection, Image resolution, Nickel, Accuracy assessment, Remote sensing

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