Model-based Nine-Component Scattering Matrix Power Decomposition

This paper aims to establish physical interpretation of nine-component scattering power decomposition using all coherency matrix elements/parameters for fully polarimetric SAR data analysis. It has been known that complete scattering mechanisms can be characterized by using all nine parameters of coherency matrix. We try to decompose the coherency matrix data in a physical scattering manner, as previously reported in a geometrical way by Huynen. New physical scattering models of real and imaginary part of T12 are introduced, which represent dipole scattering power and quarter wave plate scattering power, respectively. These models are added to the existing scattering models for ideal case of surface scattering, double-bounce scattering, and volume scattering. A quantitative analysis of the oriented urban patch of Mumbai in the L-band dataset result reveals a 9.7% decrease in volume scattering, which avoids misinterpretation between vegetation and the oriented urban area, and the 22.5% and 13.5% contributions come from dipole and quarter wave scattering powers, respectively. It is confirmed that proposed method produces better results and interpretations when compared to those by the existing decomposition methods.journal articl

An experimental Indian gravimetric geoid model using Curtin University’s approach

Over the past decade, numerous advantages of a gravimetric geoid model and its possible suitability for the Indian national vertical datum have been discussed and advocated by the Indian scientific community and national geodetic agencies. However, despite several regional efforts, a state-of-the-art gravimetric geoid model for the whole of India remains elusive due to a multitude of reasons. India encompasses one of the most diverse topographies on the planet, which includes the Gangetic plains, the Himalayas, the Thar desert, and a long peninsular coastline, among other topographic features. In the present study, we have developed the first national geoid and quasigeoid models for India using Curtin University’s approach. Terrain corrections were found to reach an extreme of 187 mGal, Faye gravity anomalies 617 mGal, and the geoid-quasigeoid separation 4.002 m. We have computed both geoid and quasigeoid models to analyse their representativeness of the Indian normal-orthometric heights from the 119 GNSS-levelling points that are available to us. A geoid model for India has been computed with an overall standard deviation of ±0.396 m but varying from ±0.03 m to ±0.158 m in four test regions with GNSS-levelling data. The greatest challenge in developing a precise gravimetric geoid for the whole of India is data availability and its preparation. More densely surveyed precise gravity data and a larger number of GNSS/levelling data are required to further improve the models and their testing

Combining evolutionary computation with machine learning technique for improved short-term prediction of UT1-UTC and length-of-day

Over the years, prediction techniques for the highly variable angular velocity of the Earth represented by Earth's rotation (UT1-UTC) and length-of-day (LOD) have been continuously improved. This is because many applications like navigation, astronomy, space exploration, climate studies, timekeeping, disaster monitoring, and geodynamic studies, all rely on predictions of these Earth rotation parameters. They provide early warning of changes in the Earth's rotation, allowing various industries and scientific fields to operate more precisely and efficiently. Thus, in our study, we focused on short-term prediction for UT1-UTC (dUT1) and LOD. Our prediction approach is to combine machine learning (ML) technique with efficient evolutionary computation (EC) algorithms to achieve reliable and improved predictions. Gaussian process regression (GPR) is used as the ML technique with genetic algorithm (GA) as the EC algorithm. GA is used for hyperparameter optimization of GPR model as selecting appropriate values for hyperparameter are essential to ensure that the prediction model can accurately capture the underlying patterns in the data. We conducted some experiments with our prediction approach to thoroughly test its capabilities. Moreover, two forecasting strategies were used to assess the performance in both hindcast and operational settings. In most of the experiments, the data used are the multi-technique combinations (C04) generated by International Earth Rotation and Reference Systems Service (IERS). In one of the experiments, we also investigated the performance of our prediction model on dUT1 and LOD from four different products obtained from IERS EOP 20 C04, DTRF20, JTRF20 and USNO. The prediction products are evaluated with real estimates of the EOP product with which the model is trained. The combined excitations of the atmosphere, oceans, hydrology, and sea level (AAM + OAM + HAM + SLAM) are used as predictors because they are highly correlated to the input data. The results depict the highest performance of 0.412 ms in dUT1 and 0.092 ms/day in LOD, on day 10 of predictions. It is worth noting that the later predictions were obtained by incorporating the uncertainty of the input data as weights in the prediction model, which was a novel approach tested in this study.Open Access funding enabled and organized by Projekt DEAL. We are grateful to DAAD Research Grants—Bi-nationally Supervised Doctoral Degrees, 2020/21 for funding the research stay of lead author (SD) in GFZ Potsdam, and the Prime Minister Research Fellowship (PMRF) for funding the doctoral study. Additionally, the lead author was awarded with Helmholtz Visiting Researcher Grant funding by the Helmholtz Information & Data Science Academy (HIDA) for working in the prediction project as a guest researcher in GFZ Potsdam for 3 months. SB was partially supported by Generalitat Valenciana SEJIGENT program (SEJIGENT/2021/001) and European Union NextGenerationEU (ZAMBRANO 21-04). JM was partially supported by Spanish Projects PID2020-119383 GB-I00 funded by MCIN/AEI/https://doi.org/10.13039/501100011033 and PROMETEO/2021/030 (Generalitat Valenciana)

Conjoint analysis for quantification of relative importance of various factors affecting BPANN classification of urban environment

This paper is an attempt to suggest an approach for eliminating the lengthy process of selecting various factors while using Backpropagation artificial neural network (BPANN) and to quantify the relative importance of the factors affecting classification results. A novel approach called conjoint analysis has been used here. The paper also presents the classification results of an Indian urban environment using two BPANN approaches and compares them with conventional Gaussian Maximum Likelihood (GML) classification approach. The study showed that conjoint analysis can be successfully used to select various parameters of BPANN prior to carrying out the classifications using any of the BPANN approach. Factors like size of training samples and first hidden layer come out as some of the most important factors while the second hidden layer has the least affect on classification accuracy. Resilient backpropagation method of BPANN is the best and robust method for urban classification. Results also showed that classification obtained using BPANN approach were similar or numerically better than GML classification though the difference was not statistically significantly different

Per-field classification of Indian urban environment using IRS-1c satellite data

The paper presents investigations to determine the suitability of conventional per-pixel approach and results of per-field (segment) classification for classifying Indian urban environment using high spatial resolution satellite data. Three different types of classifications were performed: the per-pixel classification, per-field GML classification and the per-field neural classification. Result showed that per-field classification improves overall classification accuracy up to 25% in comparison to per-pixel approach