Automated Rain Detection by Dual-Polarization Sentinel-1 Data

Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes for maritime applications. This study relies on Sentinel-1 SAR acquisitions in the Interferometric Wide swath mode and high-resolution measurements from ground-based weather radar to document the rain impact on the radar backscattered signal in both co- and cross-polarization channels. The dark and bright rain signatures are found in connection with the timeliness of the rain cells. In particular, the bright patches are demonstrated by the hydrometeors (graupels, hails) in the melting layer. In general, the radar backscatter under rain increases with rain rate for a given sea state and decreases when the sea state strengthens. The rain also has a stronger impact on the radar signal in both polarizations when the incidence angle increases. The complementary sensitivity of the SAR signal of rain in both channels is then used to derive a filter to locate the areas in SAR scenes where the signal is not dominated by rain. The filter optimized to match the rain observed by the ground-based weather radar is more efficient when both polarization channels are considered. Case studies are presented to discuss the advantages and limitations of such a filtering approach.

Download Full-text

Incidence angle normalization of Wide Swath SAR data for oceanographic applications

Open Geosciences ◽

10.1515/geo-2016-0029 ◽

2016 ◽

Vol 8 (1) ◽

Cited By ~ 20

Author(s):

Konstantinos Topouzelis ◽

Suman Singha ◽

Dimitra Kitsiou

Keyword(s):

Synthetic Aperture Radar ◽

Cross Section ◽

Incidence Angle ◽

Radar Cross Section ◽

Sea Surface ◽

Synthetic Aperture ◽

Progressive Reduction ◽

Sar Images ◽

Wide Swath

AbstractA backscattering trend in the range direction of the signal received by Synthetic Aperture Radar (SAR) in Wide Swath (WS) mode results in a progressive reduction of brightness over images from near to far range, which affects the detection and classification of sea surface features on wide swath SAR images. The aim of the present paper is to investigate methods for limiting the issue of Normalized Radar Cross-Section (NRCS or

Download Full-text

A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems

Sensors ◽

10.3390/s18103580 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3580 ◽

Cited By ~ 1

Author(s):

Jie Wang ◽

Ke-Hong Zhu ◽

Li-Na Wang ◽

Xing-Dong Liang ◽

Long-Yong Chen

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Synthetic Aperture Radar ◽

3D Imaging ◽

Synthetic Aperture ◽

Separation Scheme ◽

Sar Images ◽

Wide Swath ◽

Aperture Radar

In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems is seriously limited. This is mainly because the superposed echoes of the multiple transmitted orthogonal waveforms cannot be separated perfectly. The imperfect separation will introduce ambiguous energy and degrade SAR images dramatically. In this paper, a novel orthogonal waveform separation scheme based on echo-compression is proposed for airborne MIMO-SAR systems. Specifically, apart from the simultaneous transmissions, the transmitters are required to radiate several times alone in a synthetic aperture to sense their private inner-aperture channels. Since the channel responses at the neighboring azimuth positions are relevant, the energy of the solely radiated orthogonal waveforms in the superposed echoes will be concentrated. To this end, the echoes of the multiple transmitted orthogonal waveforms can be separated by cancelling the peaks. In addition, the cleaned echoes, along with original superposed one, can be used to reconstruct the unambiguous echoes. The proposed scheme is validated by simulations.

Download Full-text

A Wind Speed Retrieval Model for Sentinel-1A EW Mode Cross-Polarization Images

Remote Sensing ◽

10.3390/rs11020153 ◽

2019 ◽

Vol 11 (2) ◽

pp. 153 ◽

Cited By ~ 3

Author(s):

Yuan Gao ◽

Changlong Guan ◽

Jian Sun ◽

Lian Xie

Keyword(s):

Wind Speed ◽

Incidence Angle ◽

Surface Wind ◽

European Space Agency ◽

Sea Surface ◽

Cross Polarization ◽

Retrieval Model ◽

Sar Images ◽

Wind Speeds ◽

Sea Surface Wind

In contrast to co-polarization (VV or HH) synthetic aperture radar (SAR) images, cross-polarization (CP for VH or HV) SAR images can be used to retrieve sea surface wind speeds larger than 20 m/s without knowing the wind directions. In this paper, a new wind speed retrieval model is proposed for European Space Agency (ESA) Sentinel-1A (S-1A) Extra-Wide swath (EW) mode VH-polarized images. Nineteen S-1A images under tropical cyclone condition observed in the 2016 hurricane season and the matching data from the Soil Moisture Active Passive (SMAP) radiometer are collected and divided into two datasets. The relationships between normalized radar cross-section (NRCS), sea surface wind speed, wind direction and radar incidence angle are analyzed for each sub-band, and an empirical retrieval model is presented. To correct the large biases at the center and at the boundaries of each sub-band, a corrected model with an incidence angle factor is proposed. The new model is validated by comparing the wind speeds retrieved from S-1A images with the wind speeds measured by SMAP. The results suggest that the proposed model can be used to retrieve wind speeds up to 35 m/s for sub-bands 1 to 4 and 25 m/s for sub-band 5.

Download Full-text

Cross-Polarized Synthetic Aperture Radar: A New Potential Measurement Technique for Hurricanes

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-11-00001.1 ◽

2012 ◽

Vol 93 (4) ◽

pp. 531-541 ◽

Cited By ~ 114

Author(s):

Biao Zhang ◽

William Perrie

Keyword(s):

Wind Speed ◽

Synthetic Aperture Radar ◽

Wind Direction ◽

Incidence Angle ◽

Microwave Radiometer ◽

Synthetic Aperture ◽

Cross Polarization ◽

Potential Measurement ◽

Wind Retrievals ◽

Aperture Radar

We present an empirical C-band Cross-Polarization Ocean (C-2PO) model for wind retrievals from synthetic aperture radar (SAR) data collected by the RADARSAT-2 satellite. The C-2PO model relates normalized radar cross section (NRCS) in cross polarization to wind speed at 10-m height. This wind retrieval model has the characteristic that it is independent of wind direction and radar incidence angle but is quite linear with respect to wind speed. To evaluate the accuracy of the proposed model, winds with a resolution on the scale of 1 km were retrieved from a dual-polarization SAR image of Hurricane Earl on 2 September 2010, using the C-2PO model and compared with CMOD5.N, the newest available C-band geophysical model function (GMF), and validated with collocated airborne stepped-frequency microwave radiometer measurements and National Data Buoy Center data. Results suggest that for winds up to 38 m s−1, C-2PO has a bias of −0.89 m s−1 and a root-meansquare error of 3.23 m s−1 compared to CMOD5.N, which has a bias of −4.14 m s−1 and an rms difference of 6.24 m s−1. Similar results are obtained from Hurricane Ike, comparing wind retrievals from C-2PO and CMOD5.N with H*Wind data. The advantage of C-2PO over CMOD5.N and other GMFs is that it does not need any external wind direction and radar incidence angle inputs. Moreover, in the presently available quad-polarization dataset, C-2PO has the feature that the cross-polarized NRCS linearly increases even for wind speeds up to 26 m s−1 and reproduces the hurricane eye structure well, thereby providing a potential technique for hurricane observations from space.

Download Full-text

Simulations for a wide swath synthetic aperture microwave radiometric imaging of wind speed and rain rate in hurricanes

10.1117/12.850290 ◽

2010 ◽

Author(s):

Ruba A. Amarin ◽

Salem El-Nimri ◽

Suleiman Alsweiss ◽

James Johnson ◽

W. Linwood Jones

Keyword(s):

Wind Speed ◽

Rain Rate ◽

Synthetic Aperture ◽

Wide Swath

Download Full-text

A Sensitivity Analysis on the Spectral Signatures of Low-Backscattering Sea Areas in Sentinel-1 SAR Images

Remote Sensing ◽

10.3390/rs13061183 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1183 ◽

Cited By ~ 1

Author(s):

Valeria Corcione ◽

Andrea Buono ◽

Ferdinando Nunziata ◽

Maurizio Migliaccio

Keyword(s):

Oil Pollution ◽

Sea Surface ◽

Synthetic Aperture ◽

Sar Images ◽

Spectral Signatures ◽

Objective Metrics ◽

Wide Range ◽

Imaging Mode ◽

Physical Phenomena ◽

Wide Swath

Satellite synthetic aperture radar (SAR) is a unique tool to collect measurements over sea surface but the physical interpretation of such data is not always straightforward. Among the different sea targets of interest, low-backscattering areas are often associated to marine oil pollution even if several physical phenomena may also result in low-backscattering patches at sea. In this study, the effects of low-backscattering areas of anthropogenic and natural origin on the azimuth autocorrelation function (AACF) are analyzed using VV-polarized SAR measurements. Two objective metrics are introduced to quantify the deviation of the AACF evaluated over low-backscattering areas with reference to slick-free sea surface. Experiments, undertaken on six Sentinel-1 SAR scenes, collected in Interferometric Wide Swath VV+VH imaging mode over large low-backscattering areas of different origin under low-to-moderate wind conditions (speed ≤ 7 m/s), spanning a wide range of incidence angles (from about 30° up to 46°), demonstrated that the AACF evaluated within low-backscattering sea areas remarkably deviates from the slick-free sea surface one and the largest deviation is observed over oil slicks.

Download Full-text

TanDEM-X Forest Mapping Using Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs11242980 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2980 ◽

Cited By ~ 3

Author(s):

Antonio Mazza ◽

Francescopaolo Sica ◽

Paola Rizzoli ◽

Giuseppe Scarpa

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

State Of The Art ◽

Incidence Angle ◽

Synthetic Aperture ◽

Radar Signal ◽

Radar Signal Processing ◽

Forest Mapping ◽

Nonlinear Processing ◽

Baseline Approach

In this work, we face the problem of forest mapping from TanDEM-X data by means of Convolutional Neural Networks (CNNs). Our study aims to highlight the relevance of domain-related features for the extraction of the information of interest thanks to their joint nonlinear processing through CNN. In particular, we focus on the main InSAR features as the backscatter, coherence, and volume decorrelation, as well as the acquisition geometry through the local incidence angle. By using different state-of-the-art CNN architectures, our experiments consistently demonstrate the great potential of deep learning in data fusion for information extraction in the context of synthetic aperture radar signal processing and specifically for the task of forest mapping from TanDEM-X images. We compare three state-of-the-art CNN architectures, such as ResNet, DenseNet, and U-Net, obtaining a large performance gain over the baseline approach for all of them, with the U-Net solution being the most effective one.

Download Full-text

Towards a 20 m Global Building Map from Sentinel-1 SAR Data

Remote Sensing ◽

10.3390/rs10111833 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1833 ◽

Cited By ~ 12

Author(s):

Marco Chini ◽

Ramona Pelich ◽

Renaud Hostache ◽

Patrick Matgen ◽

Carlos Lopez-Martinez

Keyword(s):

Arid Regions ◽

Synthetic Aperture ◽

False Alarms ◽

Satellite Mission ◽

Sar Images ◽

Multi Temporal ◽

Interferometric Sar ◽

Very High ◽

Urban Footprint ◽

Wide Swath

This study introduces a technique for automatically mapping built-up areas using synthetic aperture radar (SAR) backscattering intensity and interferometric multi-temporal coherence generated from Sentinel-1 data in the framework of the Copernicus program. The underlying hypothesis is that, in SAR images, built-up areas exhibit very high backscattering values that are coherent in time. Several particular characteristics of the Sentinel-1 satellite mission are put to good use, such as its high revisit time, the availability of dual-polarized data, and its small orbital tube. The newly developed algorithm is based on an adaptive parametric thresholding that first identifies pixels with high backscattering values in both VV and VH polarimetric channels. The interferometric SAR coherence is then used to reduce false alarms. These are caused by land cover classes (other than buildings) that are characterized by high backscattering values that are not coherent in time (e.g., certain types of vegetated areas). The algorithm was tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa. The resulting building maps were compared with the Global Urban Footprint (GUF) derived from the TerraSAR-X mission data and, on average, a 92% agreement was obtained.

Download Full-text

The Dependence of Sea SAR Image Distribution Parameters on Surface Wave Characteristics

Remote Sensing ◽

10.3390/rs10111843 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1843 ◽

Cited By ~ 2

Author(s):

Jian Sun ◽

Xin Wang ◽

Xinzhe Yuan ◽

Qingjun Zhang ◽

Changlong Guan ◽

...

Keyword(s):

Surface Wave ◽

Synthetic Aperture Radar ◽

Target Detection ◽

Synthetic Aperture ◽

Sar Image ◽

Statistical Distributions ◽

Sea State ◽

Sar Images ◽

Distribution Parameters ◽

Aperture Radar

Modeling the statistical distribution of synthetic aperture radar (SAR) images is essential for sea target detection, which is an important aspect of marine SAR applications. The main goal of this study is to determine the effects of sea states and surface wave texture characteristics on the statistical distributions of sea SAR images. A statistical analysis of the Envisat Advanced Synthetic Aperture Radar (ASAR) wave mode images (imagettes), covering a variety of sea states and wave conditions, was carried out to investigate the suitability of the statistical distributions often used in the literature for sea states parameters. The results revealed the variation in the distribution parameters in terms of their azimuthal cutoff wavelength (ACW) and the peak-to-background ratio (PBR) of the SAR image intensity spectra. The shape parameters of Gamma and Weibull distribution are sensitive and monotonously decreasing with respect to PBR, while the scale parameter is sensitive to ACW. The K distribution was shown to perform well, with both high and stable accuracy. The results of this paper provide a parameterized scheme for sea state classifications and can potentially be used for choosing the most suitable distribution model according to sea state when performing sea target detection.

Download Full-text

MARITIME PRODUCTS USING TERRASAR-X and SENTINEL-1 IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-967-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 967-973 ◽

Cited By ~ 7

Author(s):

S. Lehner ◽

B. Tings

Keyword(s):

Remote Sensing ◽

Real Time ◽

Wind Field ◽

Oil Spills ◽

Incidence Angle ◽

Sea Surface ◽

Synthetic Aperture ◽

Sea State ◽

Radar Backscatter ◽

Sea Surface Roughness

High resolution remote sensing Synthetic Aperture Radar (SAR) data from TerraSAR-X/Tandem-X satellites are used to determine and monitor the sea surface in near real time and all weather and illumination conditions. The radar backscatter of the sea surface is determined by the sea surface roughness caused by the wind field and the sea state. These meteo parameters are modelled by the newly developed algorithms XMOD and XWAVE relating the wind field and sea state, depending on incidence angle and directionality to the radar backscatter sigma0. <br><br> The TerraSAR-X Modes Stripmap, Scan SAR and Scan SAR Wide are used together with Sentinel and RADARSAT data to detect ships, oil spills and icebergs. The detectability depending on the background conditions is discussed. Several examples from near real time campaigns performed together with users are given.

Download Full-text