Research Work

With the global population increase, our natural resources are stressed as never before, and the global day/night monitoring of the terrestrial surface/near-surface constituents affected by natural processes, man-made impact, and frequent disasters becomes all the more urgent.

From my previous research, I realized that microwave remote sensing with polarimetric radar technology must now be advanced strongly and most rapidly for surface/near-surface parameter studies. Based on previous research experiences and knowledge I intend to develop new fully polarimetric SAR remote sensing foundations and techniques by implementing next-generation radar satellites imagery for operational earth surface/sub-surface monitoring.

My Ph.D. research is on the development of a novel physical scattering model based on fully Polarimetric Synthetic Aperture Radar (PolSAR) data decomposition method and its real-world applications. It is to develop the effective microwave polarimetric radar remote sensing techniques for accurately monitoring the surface/sub-surface parameters and the changes, which will be able to produce the physical input functions as operational products with the aid of current space-borne remote sensing missions and near-future space-borne missions.

In today’s dynamic Earth context, fully polarimetric SAR data utilization has a crucial role in understanding the Earth's surface parameters and monitoring rapid changes whether they are naturally occurring changes or due to natural disasters and man-induced crisis. Although retrieval of Earth surface parameters using radar polarimetry methods was investigated for the last two decades, simple and robust algorithm/methodology is still lacking, which can be directly applied without restrictions for monitoring of the terrestrial covers. Hitherto, the capability of polarimetric radar remote sensing for monitoring the geo-/bio-environmental parameters has not been fully exploited due to the relatively short history of space-borne polarimetric synthetic aperture radar (PolSAR) sensors operations. Furthermore, the scientific challenge for accurate geo-/bio-environmental parameters retrieval is to develop adequate techniques that can describe and account for the complex natural features in its varying states while minimizing the confounding influences of both targets (scatterer) and sensor characteristics. Therefore, the main purpose of the research plan is to develop novel radar remote sensing techniques for accurately monitoring the geo-/bio-environmental parameters and the environmental changes utilizing the availability of these proven new techniques and by implementing novel discrimination techniques from the implementation of its rapid advancements.

Microwave signals have the capability to penetrate, interact with targets and acquire more information with high resolution at any given time (day/night) as compared to other optical signals. The potential of model-based decompositions is coupled with polarimetric information extraction from POLSAR data for target identification and classification. The coherency matrix [T] with nine independent parameters, and associated with some physical scattering models, serves as input to physical scattering model-based decompositions.

The proposed Decomposition model, G5U (5-component scattering power decomposition with Unitary Transformation) accounts for 7 polarization parameters out of 7 contained in the unitary transformed coherency matrix, which means it utilizes 100% polarimetric information in the decomposition model and minimizes twice T33 element due to 2 consecutive transformations, which helps to reduce the overestimating of volume scattering power in the decomposition results and improve to correctly identify urban areas.

Total Power = Ps + Pd + Pv + Pod + Poqw

Another proposed decomposition Model attempts to assign one such physical scattering model to the real part and imaginary part of T12 and develop a new scattering power decomposition model by inclusion of a generalized volume scattering model, to be denoted as the nine-component scattering decomposition (9SD). The proposed 9SD method extracts 9 scattering mechanisms using a coherency matrix by implementing compound scattering theory.

The proposed G5U decomposition model was applied on three different quad pol. datasets and compare with all existing decomposition models FDD, Y4O, Y4R, S4R, G4U, 6SD, and 7SD. G5U gives improved results, which helps to reduce the HV-component contribution in volume scattering in oriented urban structures or man-made structures about the radar line of sight, and utilized 100% polarimetric parameters of coherency matrix.

The proposed complete 9SD decomposition method is implemented on full-PolSAR data to identify the complete scattering from the coherency matrix. As result, the decomposition using the 9SD, improves the double/single scattering power values significantly as compared to the existing 7SD decomposition Model, because the 7SD produces less individual scattering power component as compared to 9SD. 9SD results help in the interpretation of compounded signals of various targets, which were not realized earlier and exist in a heterogeneous medium like a forest, agriculture field, urban areas, etc.

In the current century, the human race may face crisis such as global warming, geo-/bio-environmental degradations, and resource depletion caused by natural hazards and man-made impact, and these crises will become great threats to future generations. This unique research plan will lead to the development of geophysical descriptors as operational products with the aid of current space-borne radar remote sensing missions and near-future space-borne missions. Therefore, the potential for retrieving the geo-/bio-physical parameters with a high spatial and temporal resolution will represent the functional prospects for studying abrupt as well as slow changes in the earth surface/sub-surface; thus generating input functions for developing more reliable management and monitoring procedures of natural resources (e.g., snow, ice, forest, soil, etc.) and the disasters reduction (flood, landslide, earthquake damages responses), and for developing highly improved scattering inversion models, which have a great socio-economic impact.