Mr. Shashi Kumar Profile

Shashi Kumar

Scientist at Indian Institute of Remote Sensing

SPIE Involvement:

Author

Area of Expertise:

SAR Remote Sensing , Interferometric and Differential Interferometric SAR Remote Sensing , Polarimetric SAR (PolSAR) Remote Sensing & Hybrid SAR Polarimetry , Polarimetric SAR Interferometry (PolInSAR) , SAR Tomography , Mathematical and Physical Modeling of Microwave Scattering and SAR Remote Sensing

Profile Summary

Shashi Kumar is working as Scientist in Photogrammetry & Remote Sensing Department of Indian Institute of Remote Sensing (IIRS), ISRO, Dehradun. His research work is focused on SAR Remote Sensing with special emphasis on Polarimetric SAR (PolSAR), Polarimetric SAR Interferometry (PolInSAR) and SAR Tomography for structural and biophysical characterization of forest area. Shashi Kumar has supervised several research works on SAR Remote Sensing and its utilization for forest parameter retrieval. He is also actively involved in NISAR related activities to process the PolSAR & PolInSAR data and its utilization for various applications. He has carried out several research projects on SAR data and some of the major highlights are enumerated below.
• PolSAR and PolInSAR based model development for stem volume and aboveground biomass (AGB) estimation of forest.
• PolSAR Tomography for vertical tree structure of tropical forest.
• Processing of Hybrid-Pol (RISAT-1, FRS-1) and Quad-Pol (RISAT-1, FRS-2) SAR data for semi-empirical modeling for forest AGB estimation.
• Integration of PolSAR, PolInSAR and Terrestrial Laser Scanner (TLS) data for tree height estimation and modelling for forest AGB estimation.
• Polarimetric modeling of Lunar Surface for scattering information retrieval using Chandrayaan-1, Mini-SAR
• Hyperspectral Remote Sensing for Extraction of Aqueous Minerals on Mars and Optical Maturity Estimation of Titanium Dioxide & Ferrous Oxide on Lunar Surface.

Publications (17)

SPIE Journal Paper | 25 June 2020

Multisensor temporal approach for transplanted paddy fields mapping using fuzzy-based classifiers

Anuvi Rawat, Anil Kumar, Priyadarshi Upadhyay, Shashi Kumar

JARS Vol. 14 Issue 02

KEYWORDS: Synthetic aperture radar, Transplantation, Associative arrays, Sensors, Earth observing sensors, Landsat, Vegetation, Image classification, Fuzzy logic, Accuracy assessment

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Proceedings Article | 9 October 2018 Paper

Paper

X-band persistent SAR interferometry for surface subsidence detection in Rudrapur City, India

Akshar Tripathi, Sandeep Maithani, Shashi Kumar

Proc. SPIE. 10793, Remote Sensing Technologies and Applications in Urban Environments III

KEYWORDS: Agriculture, Polarization, Satellites, Synthetic aperture radar, Remote sensing, Interferometry, Phase interferometry, Vegetation, Buildings, X band

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SPIE Journal Paper | 26 May 2017

Performance of PolSAR backscatter and PolInSAR coherence for scattering characterization of forest vegetation using single pass X-band spaceborne synthetic aperture radar data

Sushil Kumar Joshi, Shashi Kumar

JARS Vol. 11 Issue 02

KEYWORDS: Synthetic aperture radar, Backscatter, Scattering, Vegetation, X band, Polarimetry, Interferometry, Data acquisition, Polarization

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SPIE Journal Paper | 2 January 2017

Spaceborne PolInSAR tomography for vertical profile retrieval of forest vegetation

Sushil Kumar Joshi, Shashi Kumar

JARS Vol. 11 Issue 01

KEYWORDS: Tomography, Synthetic aperture radar, Backscatter, Vegetation, Fourier transforms, Scattering, Radar, Interferometry, Reflectivity, Polarimetry

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Proceedings Article | 11 May 2016 Paper

Paper

PolInSAR tomography for vertical profile retrieval of forest vegetation using spaceborne SAR data

Sushil Joshi, Shashi Kumar, Shefali Agrawal, Ho Dinh

Proc. SPIE. 9877, Land Surface and Cryosphere Remote Sensing III

KEYWORDS: Radar, Backscatter, Synthetic aperture radar, Synthetic aperture radar, Reflectivity, Fourier transforms, Interferometry, Polarimetry, Vegetation, Vegetation, Signal processing

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Proceedings Article | 7 May 2016 Paper

Paper

Dark spot detection for characterization of marine surface slicks using PolSAR remote sensing

Shashi Kumar, Hari Kattamuri, Shefali Agarwal

Proc. SPIE. 9878, Remote Sensing of the Oceans and Inland Waters: Techniques, Applications, and Challenges

KEYWORDS: Polarization, Backscatter, Scattering, Synthetic aperture radar, Image segmentation, Remote sensing, Polarimetry, Ocean optics, Image classification, Anisotropy

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Oceans are considered as the important source for oil reserves and continuous activities like oil extraction and transportation may sometimes cause the accidental release of oil into the sea surface which causes a major threat to the marine ecosystems, economy and human life. The prime focus of this study is to explore the potential of the fully polarimetric SAR data and analyze the different scattering mechanisms for the oil spilled regions. In this study the fully polarized and orthorectified, L band data of UAVSAR airborne sensor is used which is captured on June 22nd 2010, during which the Deepwater Horizon oil spill occurred in the Gulf of Mexico. For the detection of oil spill different decomposition techniques such as Freeman, Yamaguchi and H/A/α are studied and classified using Wishart classification. Freeman and Yamaguchi decomposition helped in understanding the type of scattering mechanism taking place in slick covered regions, sea surface and in the presence of ships/rig. A set of polarimetric parameters such as magnitude of correlation coefficient, cross product of co polarized channels, anisotropy, alpha ,entropy and the intensity of the coherency matrix are studied which helped in distinguishing the oil spills, sea surface and the look-alikes. The Wishart classification result of Freeman and Yamaguchi decompositions showed more reliable results in comparison to the K-means classification results obtained through segmentation of combined H/A/α decomposition. The entropy, anisotropy and magnitude of correlation coefficient are dependent on the angle of incidence. At low incidence angle the entropy value of oil spills are similar to that of the sea surface whereas the magnitude of correlation coefficient which is a function of dielectric constant, increases for oil spills at low incidence angle. The polarimetric parameter, intensity of the coherency matrix utilizes the whole coherency matrix by calculating its determinant and proven to provide good discrimination between the oil spills and the sea surface.

Proceedings Article | 5 May 2016 Paper

Paper

Effect of polarization orientation angle shift on X-band TDM SAR COSSC product of TerraSAR-X and TanDEM-X

Asmita Gupta, Shashi Kumar, Uttara Pandey

Proc. SPIE. 9877, Land Surface and Cryosphere Remote Sensing III

KEYWORDS: Radar, Modeling, Polarization, Backscatter, Scattering, Synthetic aperture radar, Remote sensing, Polarimetry, Vegetation, X band, RGB color model

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Proceedings Article | 5 May 2016 Paper

Paper

Deorientation of PolSAR coherency matrix for volume scattering retrieval

Shashi Kumar, R. Garg, S. P. Kushwaha

Proc. SPIE. 9877, Land Surface and Cryosphere Remote Sensing III

KEYWORDS: Modeling, Polarization, Backscatter, Scattering, Synthetic aperture radar, Matrices, Image processing, Electromagnetic radiation, Polarimetry, Electromagnetic scattering

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Polarimetric SAR data has proven its potential to extract scattering information for different features appearing in single resolution cell. Several decomposition modelling approaches have been developed to retrieve scattering information from PolSAR data. During scattering power decomposition based on physical scattering models it becomes very difficult to distinguish volume scattering as a result from randomly oriented vegetation from scattering nature of oblique structures which are responsible for double-bounce and volume scattering , because both are decomposed in same scattering mechanism. The polarization orientation angle (POA) of an electromagnetic wave is one of the most important character which gets changed due to scattering from geometrical structure of topographic slopes, oriented urban area and randomly oriented features like vegetation cover. The shift in POA affects the polarimetric radar signatures. So, for accurate estimation of scattering nature of feature compensation in polarization orientation shift becomes an essential procedure. The prime objective of this work was to investigate the effect of shift in POA in scattering information retrieval and to explore the effect of deorientation on regression between field-estimated aboveground biomass (AGB) and volume scattering. For this study Dudhwa National Park, U.P., India was selected as study area and fully polarimetric ALOS PALSAR data was used to retrieve scattering information from the forest area of Dudhwa National Park. Field data for DBH and tree height was collect for AGB estimation using stratified random sampling. AGB was estimated for 170 plots for different locations of the forest area. Yamaguchi four component decomposition modelling approach was utilized to retrieve surface, double-bounce, helix and volume scattering information. Shift in polarization orientation angle was estimated and deorientation of coherency matrix for compensation of POA shift was performed. Effect of deorientation on RGB color composite for the forest area can be easily seen. Overestimation of volume scattering and under estimation of double bounce scattering was recorded for PolSAR decomposition without deorientation and increase in double bounce scattering and decrease in volume scattering was noticed after deorientation. This study was mainly focused on volume scattering retrieval and its relation with field estimated AGB. Change in volume scattering after POA compensation of PolSAR data was recorded and a comparison was performed on volume scattering values for all the 170 forest plots for which field data were collected. Decrease in volume scattering after deorientation was noted for all the plots. Regression between PolSAR decomposition based volume scattering and AGB was performed. Before deorientation, coefficient determination (R²) between volume scattering and AGB was 0.225. After deorientation an improvement in coefficient of determination was found and the obtained value was 0.613. This study recommends deorientation of PolSAR data for decomposition modelling to retrieve reliable volume scattering information from forest area.

Proceedings Article | 5 May 2016 Paper

Paper

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

Kiledar Tomar, Shashi Kumar, Valentyn Tolpekin, Sushil Joshi

Proc. SPIE. 9877, Land Surface and Cryosphere Remote Sensing III

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

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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

Paper

Feature extraction using multi-temporal fully polarimetric SAR data

Ramya M. N. S., Shashi Kumar

Proc. SPIE. 9877, Land Surface and Cryosphere Remote Sensing III

KEYWORDS: Statistical analysis, Agriculture, Backscatter, Scattering, Synthetic aperture radar, Remote sensing, Polarimetry, Feature extraction, Data acquisition, Associative arrays, Dielectric polarization, Anisotropy, Accuracy assessment

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Proceedings Article | 5 May 2016 Paper

Paper

Random forest regression modelling for forest aboveground biomass estimation using RISAT-1 PolSAR and terrestrial LiDAR data

Rohit Mangla, Shashi Kumar, Subrata Nandy

Proc. SPIE. 9879, Lidar Remote Sensing for Environmental Monitoring XV

KEYWORDS: Modeling, Data modeling, Data modeling, Scattering, LIDAR, Synthetic aperture radar, Remote sensing, Laser scattering, Polarimetry, Clouds, Laser scanners, Biological research, Neodymium

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SAR and LiDAR remote sensing have already shown the potential of active sensors for forest parameter retrieval. SAR sensor in its fully polarimetric mode has an advantage to retrieve scattering property of different component of forest structure and LiDAR has the capability to measure structural information with very high accuracy. This study was focused on retrieval of forest aboveground biomass (AGB) using Terrestrial Laser Scanner (TLS) based point clouds and scattering property of forest vegetation obtained from decomposition modelling of RISAT-1 fully polarimetric SAR data. TLS data was acquired for 14 plots of Timli forest range, Uttarakhand, India. The forest area is dominated by Sal trees and random sampling with plot size of 0.1 ha (31.62m*31.62m) was adopted for TLS and field data collection. RISAT-1 data was processed to retrieve SAR data based variables and TLS point clouds based 3D imaging was done to retrieve LiDAR based variables. Surface scattering, double-bounce scattering, volume scattering, helix and wire scattering were the SAR based variables retrieved from polarimetric decomposition. Tree heights and stem diameters were used as LiDAR based variables retrieved from single tree vertical height and least square circle fit methods respectively. All the variables obtained for forest plots were used as an input in a machine learning based Random Forest Regression Model, which was developed in this study for forest AGB estimation. Modelled output for forest AGB showed reliable accuracy (RMSE = 27.68 t/ha) and a good coefficient of determination (0.63) was obtained through the linear regression between modelled AGB and field-estimated AGB. The sensitivity analysis showed that the model was more sensitive for the major contributed variables (stem diameter and volume scattering) and these variables were measured from two different remote sensing techniques. This study strongly recommends the integration of SAR and LiDAR data for forest AGB estimation.

Proceedings Article | 5 May 2016 Paper

Paper

Polarimetric SAR interferometry-based decomposition modelling for reliable scattering retrieval

Neeraj Agrawal, Shashi Kumar, Valentyn Tolpekin

Proc. SPIE. 9877, Land Surface and Cryosphere Remote Sensing III

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

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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.

Proceedings Article | 5 May 2016 Paper

Paper

Performance of PolSAR backscatter and PolInSAR coherence for scattering characterization of forest vegetation using TerraSAR-X data

Sushil Joshi, Shashi Kumar, Shefali Agrawal

Proc. SPIE. 9877, Land Surface and Cryosphere Remote Sensing III

KEYWORDS: Radar, Coherence (optics), Polarization, Backscatter, Scattering, Synthetic aperture radar, Interferometry, Polarimetry, Vegetation, Data acquisition

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Proceedings Article | 2 May 2016 Paper

Paper

Simulation of SAR backscatter for forest vegetation

Richa Prajapati, Shashi Kumar, Shefali Agrawal

Proc. SPIE. 9881, Earth Observing Missions and Sensors: Development, Implementation, and Characterization IV

KEYWORDS: Coherence (optics), Polarization, Backscatter, Scattering, Synthetic aperture radar, Image processing, Interferometry, Computer simulations, Vegetation, Device simulation

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Synthetic Aperture Radar (SAR) is one of the most recent imaging technology to study the forest parameters. The invincible characteristics of microwave acquisition in cloudy regions and night imaging makes it a powerful tool to study dense forest regions. A coherent combination of radar polarimetry and interferometry (PolInSAR) enhances the accuracy of retrieved biophysical parameters. This paper attempts to address the issue of estimation of forest structural information caused due to instability of radar platforms through simulation of SAR image. The Terai Central Forest region situated at Haldwani area in Uttarakhand state of India was chosen as the study area. The system characteristics of PolInSAR dataset of Radarsat-2 SAR sensor was used for simulation process. Geometric and system specifications like platform altitude, center frequency, mean incidence angle, azimuth and range resolution were taken from metadata. From the field data it was observed that average tree height and forest stand density were 25 m and 300 stems/ha respectively. The obtained simulated results were compared with the sensor acquired master and slave intensity images. It was analyzed that for co-polarized horizontal component (HH), the mean values of simulated and real master image had a difference of 0.3645 with standard deviation of 0.63. Cross-polarized (HV) channel showed better results with mean difference of 0.06 and standard deviation of 0.1 while co-polarized vertical component (VV) did not show similar values. In case of HV polarization, mean variation between simulated and real slave images was found to be the least. Since cross-polarized channel is more sensitive to vegetation feature therefore better simulated results were obtained for this channel. Further the simulated images were processed using PolInSAR inversion modelling approach using three different techniques DEM differencing, Coherence Amplitude Inversion and Random Volume over Ground Inversion. DEM differencing technique calculates tree height by generating Digital Elevation Models (DEM) from interferograms in different polarizations and differences in DEM estimates the vegetation height. In CAI technique the phase of coherence is ignored and volume scattering is mainly considered for estimating height. The RVoG model considers both vegetation layer and ground interactions. In this model, the vertical distribution of scatterers do not change with the change in polarization. It was found that with vertical wavenumber values between 0.2113 to .2249 rad/m for mean incidence angle 34.226 degrees the range of tree height achieved by Coherence Amplitude Inversion and RVoG was better among the three inversion techniques.

Proceedings Article | 2 May 2016 Paper

Paper

PolSAR calibration and reconstruction of hybrid polarimetric RISAT-1 data for pseudo quad-pol decomposition: a comparison with quad-pol

Shashi Kumar, Vivek Gupta, Pratyusha Gonnuru, Sushil Kumar Joshi

Proc. SPIE. 9881, Earth Observing Missions and Sensors: Development, Implementation, and Characterization IV

KEYWORDS: Modeling, Data modeling, Polarization, Backscatter, Scattering, Calibration, Synthetic aperture radar, Synthetic aperture radar, Light scattering, Polarimetry, Reconstruction algorithms

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Proceedings Article | 30 April 2016 Paper

Paper

Estimation of lunar surface maturity and ferrous oxide from Moon Mineralogy Mapper (M3) data through data interpolation techniques

Ajith Kumar P., Shashi Kumar

Proc. SPIE. 9880, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VI

KEYWORDS: Oxides, Oxides, Minerals, Iron, Sensors, Calibration, Remote sensing, Reflectivity, Spectral resolution, Associative arrays, Chemical analysis, Analytical research, Planets, Natural surfaces, Mineralogy, Data analysis

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SPIE Journal Paper | 30 October 2012

Aboveground biomass estimation of tropical forest from Envisat advanced synthetic aperture radar data using modeling approach

Shashi Kumar, Uttara Pandey, Satya Kushwaha, Rajat Chatterjee, Wietske Bijker

JRS Vol. 6 Issue 01

KEYWORDS: Backscatter, Vegetation, Data modeling, Coherence (optics), Interferometric synthetic aperture radar, Biological research, Radar, Synthetic aperture radar, Clouds, Image retrieval

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Showing 5 of 17 publications

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