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Compact Polarimetric SAR

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 50306

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Special Issue Editors

Dr. Mohammed Dabboor

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Guest Editor

Science and Technology Branch, Environment and Climate Change Canada, Government of Canada, 2121, route Transcanadienne, 5th Floor, Office 542, Dorval, QC H9P 1J3, Canada
Interests: synthetic aperture radar (SAR); SAR satellite missions; remote sensing; earth system monitoring
Special Issues, Collections and Topics in MDPI journals

Dr. Brian Brisco

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Guest Editor

Sr. Scientist, Canada Center for Mapping and Earth Observation, Ottawa, ON, Canada
Interests: wetlands; SAR; surface water mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Suman Singha

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Guest Editor

German Aerospace Center (DLR), Remote Sensing Technology Institute, Henrich-Focke Str, 428199 Bremen, Germany
Interests: SAR polarimetry; SAR oceanography; marine and coastal applications of SAR; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Torsten Geldsetzer

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Guest Editor

1633 Broadview Road, NW, Calgary, AB T2N 3H2, Canada
Interests: SAR cryosphere applications; SAR winds

Special Issue Information

Dear Colleagues,

Fully polarimetric (FP) synthetic aperture radar (SAR) imagery is acknowledged as providing the highest performance in SAR applications, due to the complete radar target information content. However, FP SAR imagery has reduced swath width relative to single and dual polarized SAR imagery and has higher system requirements.

A SAR system with a compact polarimetric (CP) SAR architecture constitutes a significant new advancement in the field of Earth observation using radar remote sensing. A CP SAR architecture transmits circular polarization and receives two orthogonal, mutually-coherent linear polarizations. The recently proposed CP SAR configuration for Earth observation could be a compromised choice for SAR applications. The main advantage of such SAR systems is that they provide increased radar target information in comparison to standard single and dual polarized SAR systems, while covering much greater swath widths compared to FP SAR systems.

Such SAR architecture is already included in the current Japanese Advanced Land Observing Satellite (ALOS-2) carrying the Phased Array type L-band Synthetic Aperture Radar (PALSAR-2), and will be included in the future RADARSAT Constellation Mission (RCM).

This Special Issue of Remote Sensing is dedicated to demonstrate the potential of CP SAR for Earth observation applications. Articles in all SAR applications using real or simulated CP SAR data are welcome.

Dr. Mohammed Dabboor
Dr. Brian Brisco
Dr. Suman Singha
Dr. Torsten Geldsetzer
Guest Editors

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Keywords

Synthetic Aperture Radar (SAR)
SAR Compact Polarimetry
Remote Sensing
Earth Observation
SAR Applications

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Published Papers (9 papers)

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Research

Jump to: Review, Other

18 pages, 7218 KiB

Open AccessFeature PaperArticle

Combining Machine Learning and Compact Polarimetry for Estimating Soil Moisture from C-Band SAR Data

by Emanuele Santi, Mohammed Dabboor, Simone Pettinato and Simonetta Paloscia

Remote Sens. 2019, 11(20), 2451; https://doi.org/10.3390/rs11202451 - 22 Oct 2019

Cited by 28 | Viewed by 4471

Abstract

This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (σ°) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than σ°, with correlation coefficients up to R ≃ 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of σ° and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized σ°. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9. Full article

(This article belongs to the Special Issue Compact Polarimetric SAR)

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24 pages, 6786 KiB

Open AccessArticle

Synthetic Aperture Radar (SAR) Compact Polarimetry for Soil Moisture Retrieval

by Amine Merzouki, Heather McNairn, Jarrett Powers and Matthew Friesen

Remote Sens. 2019, 11(19), 2227; https://doi.org/10.3390/rs11192227 - 25 Sep 2019

Cited by 20 | Viewed by 6640

Abstract

Soil moisture is a factor for risk analysis in the agricultural sector, yet access to temporally and spatially detailed data is challenging for much of the world’s agricultural extend. Significant effort has been focused on developing methodologies to estimate soil moisture from microwave satellite sensors. Canada’s RADARSAT Constellation Mission (RCM) is capable of acquiring imagery in a number of modes with a Compact Polarimetry (CP) configuration at different spatial resolutions (1 to 100 m). RCM offers greater polarization diversity, wide swaths and improved temporal frequency (4-day exact revisit time); all important considerations for large area monitoring of agricultural resources. The major goal of this study was to examine whether CP could accurately estimate surface soil moisture over bare fields. A methodology was developed using the calibrated Integral Equation Model (IEM) multi-polarization inversion approach. RADARSAT-2 data was acquired between 2012 and 2017 over a test site in eastern Canada. CP backscatter for two RCM modes (medium resolution 30 m and 50 m (MR30 and MR50)) was simulated using 63 RADARSAT-2 fully polarimetric images. A simple transfer function was developed between RH (right circular-horizontal) and HH (horizontal-horizontal) intensity, as well as RV (right circular-vertical) and VV (vertical-vertical). These HH- and VV-like intensities were then used in the multi-polarization inversion scheme to retrieve soil moisture. CP soil moisture retrievals were validated against soil moisture measurements from a long term in-situ network instrumented with five soil moisture stations. Retrieved and measured soil moisture were well correlated (R > 0.70) with an unbiased root mean square error (ubRMSE) less than 0.06 m³/m³. Overall, the developed method clearly captured the dry down and wetting trends observed through the five years study period. However, results demonstrated that the inversion method introduced a consistent bias (~0.10 m³/m³). Comparison of CP soil moisture estimates to those from the Soil Moisture Active Passive (SMAP) passive microwave satellite confirmed this bias. This study demonstrates the potential of C-band CP data to deliver accurate soil moisture products over wide swaths for regional and national soil moisture monitoring. Full article

(This article belongs to the Special Issue Compact Polarimetric SAR)

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26 pages, 5718 KiB

Open AccessEditor’s ChoiceArticle

Mid-season Crop Classification Using Dual-, Compact-, and Full-Polarization in Preparation for the Radarsat Constellation Mission (RCM)

by Masoud Mahdianpari, Fariba Mohammadimanesh, Heather McNairn, Andrew Davidson, Mohammad Rezaee, Bahram Salehi and Saeid Homayouni

Remote Sens. 2019, 11(13), 1582; https://doi.org/10.3390/rs11131582 - 3 Jul 2019

Cited by 33 | Viewed by 6148

Abstract

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm. Full article

(This article belongs to the Special Issue Compact Polarimetric SAR)

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21 pages, 5728 KiB

Open AccessArticle

Retrieval of Oil–Water Mixture Ratio at Ocean Surface Using Compact Polarimetry Synthetic Aperture Radar

by Haiyan Li, William Perrie and Jin Wu

Remote Sens. 2019, 11(7), 816; https://doi.org/10.3390/rs11070816 - 4 Apr 2019

Cited by 13 | Viewed by 3948

Abstract

The oil–water mixture ratio for oil spills on the ocean surface is an important parameter for volume estimation of oil spills, response strategy for the oil spills, cleanup operations, and remediation planning for the impacts on wildlife. Hybrid-polarized (HP) mode compact polarization (CP) synthetic aperture radar (SAR) imagery will soon be available with the launch of the RADARSAT Constellation Mission. The advantage of the proposed new SAR system is that CP images will have wider swath and shorter revisit time compared to quad-polarization (QP) images, which are presently available from space-borne and air-borne SAR. We present a methodology to retrieve the oil–water mixture ratio at the ocean surface using CP SAR imagery. We emulated the HP mode of CP SAR image using Uninhabited Aerial Vehicle SAR (UAVSAR) L band observations collected on 23 June 2010 over the site of the Deep Water Horizon drilling rig. The gap between elements ratio of CP SAR covariance matrix and that of QP SAR Sinclair matrix is bridged. Numerical optimization and look up table methods are used to relate the oil–water mixture ratio to elements of the covariance matrix for the HP data backscatter. The mixture ratio estimates determined from the ratio of diagonal elements of the covariance matrix for HP mode CP data are compared with results retrieved from the co-polarization ratio from the original QP SAR observations. Results from the proposed methodology for SAR images captured in the HP mode of CP data are shown to compare favourably to observed in situ data of the mixture ratios. Full article

(This article belongs to the Special Issue Compact Polarimetric SAR)

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28 pages, 18416 KiB

Open AccessArticle

Wetland Classification with Multi-Angle/Temporal SAR Using Random Forests

by Sarah Banks, Lori White, Amir Behnamian, Zhaohua Chen, Benoit Montpetit, Brian Brisco, Jon Pasher and Jason Duffe

Remote Sens. 2019, 11(6), 670; https://doi.org/10.3390/rs11060670 - 19 Mar 2019

Cited by 40 | Viewed by 5386

Abstract

To better understand and mitigate threats to the long-term health and functioning of wetlands, there is need to establish comprehensive inventorying and monitoring programs. Here, remote sensing data and machine learning techniques that could support or substitute traditional field-based data collection are evaluated. For the Bay of Quinte on Lake Ontario, Canada, different combinations of multi-angle/temporal quad pol RADARSAT-2, simulated compact pol RADARSAT Constellation Mission (RCM), and high and low spatial resolution Digital Elevation and Surface Models (DEM and DSM, respectively) were used to classify six land cover classes with Random Forests: shallow water, marsh, swamp, water, forest, and agriculture/non-forested. Results demonstrate that high accuracies can be achieved with multi-temporal SAR data alone (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image and a summer image), or via fusion of SAR and DEM and DSM data for single dates/incidence angles (e.g., user’s and producer’s accuracies ≥90% for a model based on a spring image, DEM, and DSM data). For all models based on single SAR images, simulated compact pol data generally achieved lower accuracies than quad pol RADARSAT-2 data. However, it was possible to compensate for observed differences through either multi-temporal/angle data fusion or the inclusion of DEM and DSM data (i.e., as a result, there was not a statistically significant difference between multiple models). With a higher repeat-pass cycle than RADARSAT-2, RCM is expected to be a reliable source of C-band SAR data that will contribute positively to ongoing efforts to inventory wetlands and monitor change in areas containing the same land cover classes evaluated here. Full article

(This article belongs to the Special Issue Compact Polarimetric SAR)

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25 pages, 15228 KiB

Open AccessArticle

Full and Simulated Compact Polarimetry SAR Responses to Canadian Wetlands: Separability Analysis and Classification

by Fariba Mohammadimanesh, Bahram Salehi, Masoud Mahdianpari, Brian Brisco and Eric Gill

Remote Sens. 2019, 11(5), 516; https://doi.org/10.3390/rs11050516 - 3 Mar 2019

Cited by 41 | Viewed by 5504

Abstract

Detailed information on spatial distribution of wetland classes is crucial for monitoring this important productive ecosystem using advanced remote sensing tools and data. Although the potential of full- and dual-polarimetric (FP and DP) Synthetic Aperture Radar (SAR) data for wetland classification has been well examined, the capability of compact polarimetric (CP) SAR data has not yet been thoroughly investigated. This is of great significance, since the upcoming RADARSAT Constellation Mission (RCM), which will soon be the main source of SAR observations in Canada, will have CP mode as one of its main SAR configurations. This also highlights the necessity to fully exploit such important Earth Observation (EO) data by examining the similarities and dissimilarities between FP and CP SAR data for wetland mapping. Accordingly, this study examines and compares the discrimination capability of extracted features from FP and simulated CP SAR data between pairs of wetland classes. In particular, 13 FP and 22 simulated CP SAR features are extracted from RADARSAT-2 data to determine their discrimination capabilities both qualitatively and quantitatively in three wetland sites, located in Newfoundland and Labrador, Canada. Seven of 13 FP and 15 of 22 CP SAR features are found to be the most discriminant, as they indicate an excellent separability for at least one pair of wetland classes. The overall accuracies of 87.89%, 80.67%, and 84.07% are achieved using the CP SAR data for the three wetland sites (Avalon, Deer Lake, and Gros Morne, respectively) in this study. Although these accuracies are lower than those of FP SAR data, they confirm the potential of CP SAR data for wetland mapping as accuracies exceed 80% in all three sites. The CP SAR data collected by RCM will significantly contribute to the efforts ongoing of conservation strategies for wetlands and monitoring changes, especially on large scales, as they have both wider swath coverage and improved temporal resolution compared to those of RADARSAT-2. Full article

(This article belongs to the Special Issue Compact Polarimetric SAR)

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Review

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17 pages, 3972 KiB

Open AccessReview

Hybrid Dual-Polarization Synthetic Aperture Radar

by R. Keith Raney

Remote Sens. 2019, 11(13), 1521; https://doi.org/10.3390/rs11131521 - 27 Jun 2019

Cited by 43 | Viewed by 7730

Abstract

Compact polarimetry for a synthetic aperture radar (SAR) system is reviewed. Compact polarimetry (CP) is intended to provide useful polarimetric image classifications while avoiding the disadvantages of space-based quadrature-polarimetric (quad-pol) SARs. Two CP approaches are briefly described, π/4 and circular. A third form, hybrid compact polarimetry (HCP) has emerged as the preferred embodiment of compact polarimetry. HCP transmits circular polarization and receives on two orthogonal linear polarizations. When seen through its associated data processing and image classification algorithms, HPC’s heritage dates back to the Stokes parameters (1852), which are summarized and explained in plain language. Hybrid dual-polarimetric imaging radars were in the payloads of two lunar-orbiting satellites, India’s Earth-observing RISAT-1, and Japan’s ALOS-2. In lunar or planetary orbit, a satellite equipped with an HCP imaging radar delivers the same class of polarimetric information as Earth-based radar astronomy. In stark contrast to quad-pol, compact polarimetry is compatible with wide swath modes of a SAR, including ScanSAR. All operational modes of the SARs aboard Canada’s three-satellite Radarsat Constellation Mission (RCM) are hybrid dual-polarimetric. Image classification methodologies for HCP data are reviewed, two of which introduce errors for reasons explained. Their use is discouraged. An alternative and recommended group of methodologies yields reliable results, illustrated by polarimetrically classified images. A survey over numerous quantitative studies demonstrates HCP polarimetric classification effectiveness. The results verify that the performance accuracy of the HCP architecture is comparable to the accuracy delivered by a quadrature-polarized SAR. Four appendices are included covering related topics, including comments on inflight calibration of an HCP radar. Full article

(This article belongs to the Special Issue Compact Polarimetric SAR)

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Other

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15 pages, 11432 KiB

Open AccessLetter

Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images

by Qiancong Fan, Feng Chen, Ming Cheng, Shenlong Lou, Rulin Xiao, Biao Zhang, Cheng Wang and Jonathan Li

Remote Sens. 2019, 11(18), 2171; https://doi.org/10.3390/rs11182171 - 18 Sep 2019

Cited by 53 | Viewed by 5187

Abstract

Compact polarimetric synthetic aperture radar (CP SAR), as a new technique or observation system, has attracted much attention in recent years. Compared with quad-polarization SAR (QP SAR), CP SAR provides an observation with a wider swath, while, compared with linear dual-polarization SAR, retains more polarization information in observations. These characteristics make CP SAR a useful tool in marine environmental applications. Previous studies showed the potential of CP SAR images for ship detection. However, false alarms, caused by ocean clutter and the lack of detailed information about ships, largely hinder traditional methods from feature selection for ship discrimination. In this paper, a segmentation method designed specifically for ship detection from CP SAR images is proposed. The pixel-wise detection is based on a fully convolutional network (i.e., U-Net). In particular, three classes (ship, land, and sea) were considered in the classification scheme. To extract features, a series of down-samplings with several convolutions were employed. Then, to generate classifications, deep semantic and shallow high-resolution features were used in up-sampling. Experiments on several CP SAR images simulated from Gaofen-3 QP SAR images demonstrate the effectiveness of the proposed method. Compared with Faster RCNN (region-based convolutional neural network), which is considered a popular and effective deep learning network for object detection, the newly proposed method, with precision and recall greater than 90% and a F₁ score of 0.912, performs better at ship detection. Additionally, findings verify the advantages of the CP configuration compared with single polarization and linear dual-polarization. Full article

(This article belongs to the Special Issue Compact Polarimetric SAR)

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