Polarimetric Calibration and Quality Assessment of the GF-3 Satellite Images

Chang, Y.; Li, Pingxiang; Zhao, Jinqi; Zhao, Lingli; Shi, Lei

doi:10.3390/s18020403

Cited by 32 publications

(26 citation statements)

References 24 publications

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“…With a design life of eight years [43], Gaofen-3 has been in operation officially since January 2017 [42]. The Gaofen-3 SAR image, which meets the accuracy for ship detection [44,45], is competent in numerous applications, such as in monitoring the global ocean and land resources [46]. Used in the experiments were eight Gaofen-3 QP SAR images, provided by the China Centre for Resource Satellite Data and Applications (CRESDA, http://www.cresda.com).…”

Section: Datamentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Datamentioning

confidence: 99%

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

et al. 2019

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“…Moreover, this instrument has the ability to acquire fully polarized data, i.e., combinations of VV, HH, VH, and HV polarizations. Several papers [38,39] have described the technical specifications of GF-3, and we will not further address this issue. One of the primary tasks of GF-3 is ocean and coastal observation since a spaceborne SAR instrument was previously absent among the Chinese ocean-observing satellites [40].…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Sea Surface Wind Speeds from Gaofen-3 Full Polarimetric Data

TianYu

Qian

et al. 2019

Remote Sensing

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In this paper, the sea surface wind speed (SSWS) retrieval from Gaofen-3 (GF-3) quad-polarization stripmap (QPS) data in vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) polarizations is investigated in detail based on 3170 scenes acquired from October 2016 to May 2018. The radiometric calibration factor of the VV polarization data is examined first. This calibration factor generally meets the requirement of SSWS retrieval accuracy with an absolute bias of less than 0.5 m/s but shows highly dispersed characteristics. These results lead to SSWS retrievals with a small bias of 0.18 m/s, but a rather high root mean square error (RMSE) of 2.36 m/s when compared with the ERA-Interim reanalysis model data. Two refitted polarization ratio (PR) models for the QPS HH polarization data are presented. Based on a combination of the incidence angle-dependent and azimuth angle-dependent PR model and CMOD5.N, the SSWS derived from the QPS HH data shows a bias of 0.07 m/s and an RMSE of 2.26 m/s relative to the ERA-Interim reanalysis model wind speed. A linear function relating SSWS and the normalized radar cross section (NRCS) of QPS VH data is derived. The SSWS data retrieved from the QPS VH data show good agreement with the WindSat SSWS data, with a bias of 0.1 m/s and an RMSE of 2.02 m/s. We also apply the linear function to the GF-3 Wide ScanSAR data acquired for the typhoon SOULIK, which yields very good agreement with the model results. A comparison of SSWS retrievals among three different polarization datasets is also presented. The current study and our previous work demonstrate that the general accuracy of the SSWS retrieval based on GF-3 QPS data has an absolute bias of less than 0.3 m/s and an RMSE of 2.0 ± 0.2 m/s relative to various datasets. Further improvement will depend on dedicated radiometric calibration efforts.

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“…However, the polarimetric distortion in the Polarimetric Synthetic Aperture Radar (PolSAR) datasets causes the polarimetric decomposition and classification techniques to produce wrong outputs, which result in misinterpreting of the scattering phenomenon and ground targets. The channel imbalance, phase bias, and crosstalk are the various types of polarimetric distortions caused due to the system non-idealities and the Faraday rotation error is the polarimetric distortion induced due to the atmosphere, which is more dominant for lowfrequency SAR systems operating in the L-band and P-band [6].…”

Section: Introductionmentioning

confidence: 99%

RISAT-1 Compact Polarimetric Calibration and Decomposition

Babu

Kumar

Agrawal

2019

3rd International Electronic Conference on Remote Sensing

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Indian Space Research Organisation’s Radar Imaging Satellite (RISAT) -1 was the first Synthetic Aperture Radar (SAR) satellite equipped with the compact polarimetric (CP) mode for data acquisition. To exploit the advantages offered by the CP mode, the datasets need to be polarimetrically calibrated. The polarimetric calibration procedure estimates the polarimetric distortions in the datasets caused due to channel imbalance, crosstalk, and Faraday rotation. These polarimetric distortions cause the misinterpretation of the ground targets in the polarimetric decomposition techniques. The Freeman compact-pol polarimetric calibration algorithm is the most commonly used algorithm. In this study, the RISAT-1 Circular Transmit Linear Receive (CTLR) dataset of the RISAT Cal Val site was used to estimate the polarimetric distortion parameters and these distortion parameters were used to polarimetrically calibrate the RISAT-1 CTLR dataset of the Doon Valley region, Uttarakhand, India. The Cloude compact-pol decomposition algorithm was used to evaluate the ground target characterization accuracy before and after polarimetric calibration using the Freeman compact-pol polarimetric calibration algorithm. Before polarimetric calibration, urban targets were showing surface scattering behavior and river channels were showing increased double-bounce scattering behavior. After polarimetric calibration, the urban targets showed dominance in double-bounce scattering and river channels showed dominance in surface scattering as per the theory.

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Polarimetric Calibration and Quality Assessment of the GF-3 Satellite Images

Cited by 32 publications

References 24 publications

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

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

Retrieval of Sea Surface Wind Speeds from Gaofen-3 Full Polarimetric Data

RISAT-1 Compact Polarimetric Calibration and Decomposition

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