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Author: search.filters.author.Rohil, Mukesh KumarSubject: search.filters.subject.Remote sensing

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    CYGNSS-derived soil moisture: Status, challenges and future
    (Elsevier, 2022-07) Rohil, Mukesh Kumar
    In the last few years, Global Navigation Satellite System-Reflectometry (GNSS-R) technology has provided an exciting solution to retrieve fine-scale spatiotemporal soil moisture predictions. The most promising data source for this task has been Cyclone Global Navigation Satellite System (CYGNSS) of the National Aeronautics and Space Administration (NASA). Several methodologies have been proposed in the literature to repurpose CYGNSS data for soil moisture estimation. In this work, we first describe the theoretical background of GNSS-R based soil moisture retrieval. We discuss the challenges associated with using GNSS-R (in particular CYGNSS) data, and present the current issues in the CYGNSS-based methodologies for estimating soil moisture. Some of the key limitations identified are in the areas of observation geometry, improper consideration of coherent and incoherent reflections, effect of vegetation on CYGNSS recording and consideration of ancillary data sources. We finally discuss potential Machine Learning based solutions for soil moisture estimation, which may provide researchers a path forward to achieve low error estimates.
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    Feature based remote sensing image registration techniques: a comprehensive and comparative review
    (Taylor & Francis, 2022-08) Rohil, Mukesh Kumar
    Earth observation using remote sensing data is a trending research field across the globe. In this perspective, image registration is a mandatory data processing step for any kind of time series data analytics. Feature-based image registration is one of the prominent categories for superimposing multi-temporal and multi-modal images over each other. The paper provides a comprehensive and comparative survey of feature detection/description techniques for remote sensing images. In addition, outlier removal algorithms are explored in detail to generate putative keypoint correspondences for accurate transformation parameter estimation. The experiments are conducted on multiple remote sensing image pairs comprising visible, Synthetic Aperture Radar (SAR) and infrared images that provide diverse characteristics of the feature target. The co-registration accuracy is quantified for all possible combinations of feature detection/description with outlier removal, and best amalgamation is visually verified to check sub-pixel spatial alignment by swiping multi-temporal or multi-modal images over each other. It has been found that SIFT performs better in optical image registration, whereas A-KAZE feature detection has an upper edge at SAR image registration task. Marginalizing Sample Consensus (MAGSAC) and Mode Guided (MG) based outlier removal techniques achieves better pruning performance for multi-temporal optical and SAR images respectively. The comparative evaluation indicates that Motion Smoothness Constraint (MSC) optimization shows optimal performance for multi-modal remote sensing images. The best possible Root Mean Square Error (RMSE) achieved for multi-temporal optical images is 0.44 pixel whereas for multi-temporal SAR images, it is 0.46 pixels. The RMSE achieved for multi-modal images is 0.48 pixel using combination of SIFT with MSC.
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    A novel country-level integrated image mosaic system using optical remote sensing imagery
    (Springer, 2022-09) Rohil, Mukesh Kumar
    Remote sensing image mosaicking is an essential processing step in generating large area coverage map using multi-temporal image scenes/strips. The mosaic data is useful for various space-borne applications that span across national level crop assessment, wetland monitoring, and snow and glacier studies, to derive important environmental indicators for sustainable development. This article highlights a novel image mosaicking processing workflow that ingests input geo-referenced image strips with sufficient overlap in-between, and generates country-level mosaic data product. The procedure takes care of large-sized geo-referenced image’s handling and re-projection, and makes data ready for mosaic processing. We have developed strip geo-registration method using Scale Invariant Feature Transform (SIFT) and Mode Biased Random Sample Consensus (MB-RANSAC) outlier removal technique to achieve sub-pixel registration accuracy. Image stitching workflow ingests co-registered image strips, and performs overlap extraction, seamline detection using multi-frame joint strategy, and image blending using region-based statistics in an automatic manner. The mosaic system has been evaluated with Resourcesat’s medium resolution optical remote sensing images over Indian subcontinent, and it has been confirmed that the common region among image strips attains required radiometric and geometric fidelity after correction. It also has been found that the average spectra deviation is less than 0.127% at different classes.