Modifying the Yamaguchi Four-Component Decomposition Scattering Powers Using a Stochastic Distance

Model-based decompositions have gained considerable attention after the initial work of Freeman and Durden. This decomposition which assumes the target to be reflection symmetric was later relaxed in the Yamaguchi et al. decomposition with the addition of the helix parameter. Since then many decomposition have been proposed where either the scattering model was modified to fit the data or the coherency matrix representing the second order statistics of the full polarimetric data is rotated to fit the scattering model. In this paper we propose to modify the Yamaguchi four-component decomposition (Y4O) scattering powers using the concept of statistical information theory for matrices. In order to achieve this modification we propose a method to estimate the polarization orientation angle (OA) from full-polarimetric SAR images using the Hellinger distance. In this method, the OA is estimated by maximizing the Hellinger distance between the un-rotated and the rotated $T_{33}$ and the $T_{22}$ components of the coherency matrix $\mathbf{[T]}$. Then, the powers of the Yamaguchi four-component model-based decomposition (Y4O) are modified using the maximum relative stochastic distance between the $T_{33}$ and the $T_{22}$ components of the coherency matrix at the estimated OA. The results show that the overall double-bounce powers over rotated urban areas have significantly improved with the reduction of volume powers. The percentage of pixels with negative powers have also decreased from the Y4O decomposition. The proposed method is both qualitatively and quantitatively compared with the results obtained from the Y4O and the Y4R decompositions for a Radarsat-2 C-band San-Francisco dataset and an UAVSAR L-band Hayward dataset.Comment: Accepted for publication in IEEE J-STARS (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Soil Moisture Retrieval During Crop Growth Cycle Using Satellite SAR Time Series

Satellite SAR-based soil moisture retrieval over agricultural fields, under crop overlain conditions, is a challenging exercise. This is so because the overlying crop volume interacts with both the incoming and the backscattered radar signal. Therefore, the soil moisture linked solely to the top layer (0–5 cm) of the soil cannot be reliably retrieved under such conditions without avoiding the obscuring effect of growing crop volume. In this investigation, we demonstrated a proof-of-concept for a time-series approach to retrieve soil moisture during crop growth cycle. Contrary to the use of the single-scene approach, the novelty of the proposed approach lies in exploiting the satellite SAR time series acquired during a cropping cycle. The proposed time-series approach is effective for capturing the nuances in the crop phenological stages while calibrating the Dubois–water cloud model (WCM) soil moisture retrieval model. By employing this approach, we achieved the 0.04 $\text{m}^{3}\;\text{m}^{-3}$ soil moisture retrieval root-mean-square error benchmark at a high spatial resolution and addressed the issue of solving for the Dubois–WCM model constants under data-constrained conditions. Furthermore, we observed that the combination of temporally non-overlapping vegetation descriptors (optical and SAR) resulted in degradation in the performance of the retrievals and under such circumstances single polarimetric descriptor performed better

Feature Selection for Edge Detection in PolSAR Images

Edge detection is one of the most critical operations for moving from data to information. Finding edges between objects is relevant for image understanding, classification, segmentation, and change detection, among other applications. The Gambini Algorithm is a good choice for finding evidence of edges. It finds the point at which a function of the difference of properties is maximized. This algorithm is very general and accepts many types of objective functions. We use an objective function built with likelihoods. Imaging with active microwave sensors has a revolutionary role in remote sensing. This technology has the potential to provide high-resolution images regardless of the Sun’s illumination and almost independently of the atmospheric conditions. Images from PolSAR sensors are sensitive to the target’s dielectric properties and structures in several polarization states of the electromagnetic waves. Edge detection in polarimetric synthetic-aperture radar (PolSAR) imagery is challenging because of the low signal-to-noise ratio and the data format (complex matrices). There are several known marginal models stemming from the complex Wishart model for the full complex format. Each of these models renders a different likelihood. This work generalizes previous studies by incorporating the ratio of intensities as evidence for edge detection. We discuss solutions for the often challenging problem of parameter estimation. We propose a technique which rejects edge estimates built with thin evidence. Using this idea of discarding potentially irrelevant evidence, we propose a technique for fusing edge pieces of evidence from different channels that only incorporate those likely to contribute positively. We use this approach for both edge and change detection in single- and multilook images from three different sensors

Feature Selection for Edge Detection in PolSAR Images

Edge detection is one of the most critical operations for moving from data to information. Finding edges between objects is relevant for image understanding, classification, segmentation, and change detection, among other applications. The Gambini Algorithm is a good choice for finding evidence of edges. It finds the point at which a function of the difference of properties is maximized. This algorithm is very general and accepts many types of objective functions. We use an objective function built with likelihoods. Imaging with active microwave sensors has a revolutionary role in remote sensing. This technology has the potential to provide high-resolution images regardless of the Sun’s illumination and almost independently of the atmospheric conditions. Images from PolSAR sensors are sensitive to the target’s dielectric properties and structures in several polarization states of the electromagnetic waves. Edge detection in polarimetric synthetic-aperture radar (PolSAR) imagery is challenging because of the low signal-to-noise ratio and the data format (complex matrices). There are several known marginal models stemming from the complex Wishart model for the full complex format. Each of these models renders a different likelihood. This work generalizes previous studies by incorporating the ratio of intensities as evidence for edge detection. We discuss solutions for the often challenging problem of parameter estimation. We propose a technique which rejects edge estimates built with thin evidence. Using this idea of discarding potentially irrelevant evidence, we propose a technique for fusing edge pieces of evidence from different channels that only incorporate those likely to contribute positively. We use this approach for both edge and change detection in single- and multilook images from three different sensors

Performance Assessment of Optical Satellite-Based Operational Snow Cover Monitoring Algorithms in Forested Landscapes

International audienceForest cover is a crucial factor that influences the performance of optical satellite-based snow cover monitoring algorithms. However, evaluation of such algorithms in forested landscapes is rare due to lack of reliable in situ data in such regions. In this investigation, we assessed the performance of the operational snow detection (SCA) and fractional snow cover estimation (FSC) algorithms employed by the Copernicus Land Monitoring Service for High-Resolution Snow & Ice Monitoring (HRSI) with a combination of Sentinel-2 and Landsat-7/8 satellite scenes, lidar-based, and in situ datasets. These algorithms were evaluated over test sites located in the forested mountainous landscape of the Pyrenees in Spain and the Sierra Nevada in the USA. Over the Pyrenees site, the effectiveness of snow cover detection was evaluated with respect to a time-series of in situ snow depth measurements logged over test plots with different aspects, canopy cover, and solar irradiance. Over the Sierra Nevada site, the impact of ground vegetation was assessed over the under canopy fractional snow cover retrievals using airborne lidar-derived fractional vegetation cover information. The analyses over the Pyrenees indicated a good accuracy of snow detection with the exception of plots with eithe

A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya

On 7 Feb 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. Over 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27x106 m3 of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders >20 m in diameter, and scoured the valley walls up to 220 m above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.ISSN:0036-8075ISSN:1095-920