Dual-Polarimetric C- and X-Band SAR Data for Coastline Extraction

Nunziata, Ferdinando; Buono, Andrea; Migliaccio, Maurizio; Benassai, Guido

doi:10.1109/jstars.2016.2560342

Cited by 71 publications

(64 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By reflecting the radar signal away from its transmitting source, water has a much lower return signal than land, which rougher texture scatters the incoming signal in all directions. A variety of image segmentation techniques have been proposed to leverage this contrast, including thresholding, fuzzy classification, and region growing (Demir et al, ; Nunziata et al, ; Vandebroek et al, ). However, radar backscattering on the Earth surface is also affected by a range of additional factors, including topography, soil moisture, and vegetation (Clement et al, , see), which confound the effect of surface roughness.…”

Section: Shoreline Detection: Sentinel 1 Split‐based Classificationmentioning

confidence: 99%

Impact of Hurricane Maria on Beach Erosion in Puerto Rico: Remote Sensing and Causal Inference

Valentín

Müller

2020

Geophysical Research Letters

View full text Add to dashboard Cite

Beach erosion due to large storms critically affects coastal vulnerability, but is challenging to monitor and quantify. Attributing erosion to a specific storm requires a reliable counterfactual scenario: hypothetical beach conditions, absent the storm. Calibrating models to construct counterfactuals requires numerous observations that are rarely available. Storm paths are unpredictable, making long‐term instrumentation of specific beaches costly. Optical remote sensing is hampered by persistent cloud cover. We use Sentinel‐1 satellite radar imagery to monitor shoreline changes through clouds and propose regression discontinuity as a strategy to estimate the causal effect of large storms on beach erosion. Applied to 75 beaches across Puerto Rico, the approach detects shoreline changes with a root‐mean‐square error comparable to the resolution of the imagery. Hurricane Maria caused an erosion of 3 to 5 m along its path, up to 40 m at particular beaches. Results reveal strong local disparities that are consistent with simulated nearshore hydrodynamic conditions.

show abstract

Section: Shoreline Detection: Sentinel 1 Split‐based Classificationmentioning

confidence: 99%

Impact of Hurricane Maria on Beach Erosion in Puerto Rico: Remote Sensing and Causal Inference

Valentín

Müller

2020

Geophysical Research Letters

View full text Add to dashboard Cite

show abstract

“…For the development of global or continental scale services and data production for ecosystem monitoring a highly reliable and stable data processing framework is required. The proposed approach of border noise removal will play a crucial role both in data processing [6] and applications [7]- [9].…”

Section: Problem Descriptionmentioning

confidence: 99%

Methods to Remove the Border Noise From Sentinel-1 Synthetic Aperture Radar Data: Implications and Importance For Time-Series Analysis

Ali

Cao

Naeimi

et al. 2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Abstract-The Sentinel-1 GRD (ground range detected) Level-1 product generated by the Instrument Processing Facil-ity of the European Space Agency has noise artifacts at the image borders, which are quite consistent at both left and right sides of the satellite's cross track and at the start and end of the data take along track. The Sentinel-1 border noise troubles the creation of clean and consistence time series of backscatter. Data quality control and management become very challenging tasks, when it comes to the large-scale data processing, both in terms of spatial coverage and data volume. In this paper, we evaluate three techniques for removing the Sentinel-1 border noise and compare the results with the existing "Sentinel-1 GRD Border Noise Removal" algorithm implemented in the Sentinel-1 toolbox of the Sentinel application platform.1 Validation and evaluation of the newly pro-posed algorithms was done using random samples containing 1500 Sentinel-1 scenes selected from a complete Sentinel-1 archive. The newly proposed approach has successfully achieved the required level of accuracy and solved the issue of time-series anomalies due to the border noise.Index Terms-C-band synthetic aperture radar (SAR), interfer-ometric wide swath (IW), Sentinel-1, Sentinel application platform (SNAP), Sentinel-1 border noise.quality insurance of large amounts of data at the scale of several hundreds of terabytes to petabyte have been becoming a challenging task.The recently launched constellation of two C-band synthetic aperture radar satellites named Sentinel-1 (A and B) is getting immense attention from both scientists and commercial users. Due to the open access data distribution policy and improved data quality in terms of temporal and spatial resolution, the Sentinel-1 user community is growing very fast. Daily global data acquisition of Sentinel-1 A and B is more than 1.5 TB [1]. In order to handle and process such big data for regional to global scale monitoring, reliable and stable processing chains are required.In this paper, we address the issue of image border noise in the Sentinel-1 GRDH (ground-range-detected high resolution) Level-1 product for large-scale data processing. The proposed methods are self-adaptive and are applicable to other acquisition modes, however, fine-tuning might be needed.

show abstract

“…The camera was calibrated every time that it was removed from the waterproof housing (i.e., when profiler was operating). The calibration procedure consists of determining the relative position of a minimum of four points ("targets") in order to find the initial location and orientation of the camera relative to the calibration frame, as described by [63][64][65][66][67]. Since the position of instruments (captured in the video records) was known, eight "target" points were used, enhancing the quality of analysis.…”

Section: Influence On Swash Hydrodynamicsmentioning

confidence: 99%

Low Frequency Waves Detected in a Large Wave Flume under Irregular Waves with Different Grouping Factor and Combination of Regular Waves

Riefolo

Contestabile

Dentale

et al. 2018

Water

Self Cite

View full text Add to dashboard Cite

This paper describes a set of experiments undertaken at Universitat Politècnica de Catalunya in the large wave flume of the Maritime Engineering Laboratory. The purpose of this study is to highlight the effects of wave grouping and long-wave short-wave combinations regimes on low frequency generations. An eigen-value decomposition has been performed to discriminate low frequencies. In particular, measured eigen modes, determined through the spectral analysis, have been compared with calculated modes by means of eigen analysis. The low frequencies detection appears to confirm the dependence on groupiness of the modal amplitudes generated in the wave flume. Some evidence of the influence of low frequency waves on runup and transport patterns are shown. In particular, the generation and evolution of secondary bedforms are consistent with energy transferred between the standing wave modes.

show abstract

Dual-Polarimetric C- and X-Band SAR Data for Coastline Extraction

Cited by 71 publications

References 17 publications

Impact of Hurricane Maria on Beach Erosion in Puerto Rico: Remote Sensing and Causal Inference

Impact of Hurricane Maria on Beach Erosion in Puerto Rico: Remote Sensing and Causal Inference

Methods to Remove the Border Noise From Sentinel-1 Synthetic Aperture Radar Data: Implications and Importance For Time-Series Analysis

Low Frequency Waves Detected in a Large Wave Flume under Irregular Waves with Different Grouping Factor and Combination of Regular Waves

Contact Info

Product

Resources

About