Analysis on the Effects of SAR Imaging Parameters and Environmental Conditions on the Standard Deviation of the Co-Polarized Phase Difference Measured over Sea Surface

Buono, Andrea; Macedo, Carina Regina de; Nunziata, Ferdinando; Velotto, Domenico; Migliaccio, Maurizio

doi:10.3390/rs11010018

Cited by 14 publications

(9 citation statements)

References 43 publications

(71 reference statements)

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“…σ φ CO has been found to increase with the presence of oil (see, e.g., [54]- [57]), and this behavior was according to [54], [55] explained by the different scattering process (Bragg vs. non-Bragg) between clean sea and oil. Some studies have showed that the broadening of φ CO could be due to system noise (see, e.g., [10], [16], [43]). Minchew et al [10] discovered no difference between the σ φ CO for oil and clean sea except in the presence of instrument noise for one UAVSAR scene.…”

Section: Scattering Properties and Polarimetric Sar Featuresmentioning

confidence: 99%

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The Impact of System Noise in Polarimetric SAR Imagery on Oil Spill Observations

Espeseth

Brekke

Jones

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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The effects of both system additive and multiplicative noise on X-, C-, and L-band Synthetic Aperture Radar (SAR) data covering oil slicks are examined. Prior studies have attempted to characterize such oil slicks, primarily through analysis of polarimetric SAR data. Here we factor in system noise that is added to the backscattered signal, introducing artifacts that can easily be confused with random and volume scattering. This confusion occurs when additive and/or multiplicative system noise dominates the measured backscattered signal. Polarimetric features used in this study are shown to be affected by both additive and multiplicative system noise, some more than others. This study highlights the importance of considering specifically multiplicative noise in the estimation of the signal-to-noise ratio (SNR). The SNR based on additive noise should at least be above 10dB. The SNR involving both additive and multiplicative noise should at least be above 0dB. The SNR from TerraSAR-X and Radarsat-2 is below 0dB for the majority of the oil slick pixels when considering both the additive and multiplicative noise, rendering these data unsuitable for any analysis of the scattering properties and characterization. These results are in contrast to the reduced impact of noise on oil slicks detected by the Lband UAVSAR system. In particular, we find there is no need to invoke exotic scattering mechanisms to explain characteristics of the data. We also recommend a noise subtraction for any polarimetric scattering analysis.

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Section: Scattering Properties and Polarimetric Sar Featuresmentioning

confidence: 99%

“…When studying oil slicks with SAR, multiplicative noise has traditionally not been considered (see, e.g., [5], [10], [13]- [16]). The multiplicative noise raises the noise floor, hence reducing the overall SNR in the data.…”

Section: Introductionmentioning

confidence: 99%

The Impact of System Noise in Polarimetric SAR Imagery on Oil Spill Observations

Espeseth

Brekke

Jones

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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“…The curve for targets under the multi-polarimetric feature set selected based on the J–M distance presents its own trend characteristics. However, previous studies have concluded that, in reality, the polarimetric characteristics performance of an oil slick are affected by factors including SAR system acquisition parameters (e.g., incident angles, NESZ, and resolution), ocean environmental conditions (e.g., wind speed/direction, sea currents, waves, sea temperature, and seawater composition), and intrinsic oil slick information (e.g., thickness, oil type, weathering degree, and cause of formation) [2,15,19,29,59,60,61]. Therefore, considering the situation described above, the proposed method uses statistical analysis to select the mean of the target sample region in order to construct the multi-polarimetric features model.…”

Section: Methodsmentioning

confidence: 99%

Marine Oil Slick Detection Based on Multi-Polarimetric Features Matching Method Using Polarimetric Synthetic Aperture Radar Data

Liu

et al. 2019

Sensors

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Polarimetric synthetic aperture radar is an important tool in the effective detection of marine oil spills. In this study, two cases of Radarsat-2 Fine mode quad-polarimetric synthetic aperture radar datasets are exploited to detect a well-known oil seep area that collected over the Gulf of Mexico using the same research area, sensor, and time. A novel oil spill detection scheme based on a multi-polarimetric features model matching method using spectral pan-similarity measure (SPM) is proposed. A multi-polarimetric features curve is generated based on optimal polarimetric features selected using Jeffreys–Matusita distance considering its ability to discriminate between thick and thin oil slicks and seawater. The SPM is used to search for and match homogeneous unlabeled pixels and assign them to a class with the highest similarity to their spectral vector size, spectral curve shape, and spectral information content. The superiority of the SPM for oil spill detection compared to traditional spectral similarity measures is demonstrated for the first time based on accuracy assessments and computational complexity analysis by comparing with four traditional spectral similarity measures, random forest (RF), support vector machine (SVM), and decision tree (DT). Experiment results indicate that the proposed method has better oil spill detection capability, with a higher average accuracy and kappa coefficient (1.5–7.9% and 1–25% higher, respectively) than the four traditional spectral similarity measures under the same computational complexity operations. Furthermore, in most cases, the proposed method produces valuable and acceptable results that are better than the RF, SVM, and DT in terms of accuracy and computational complexity.

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“…Furthermore, the standard deviation of the CPD has been shown almost independent of the estimation window size and unbiased when low correlation of co-polarized channels applies [19,20]. It is important to highlight that the std CPD behavior is affected by incidence angle, noise equivalent sigma zero (NESZ), and meteo-marine conditions as discussed by [21][22][23].…”

Section: Introductionmentioning

confidence: 95%

Internal Wave Dark-Band Signatures in ALOS-PALSAR Imagery Revealed by the Standard Deviation of the Co-Polarized Phase Difference

Macedo

Silva

2020

Remote Sensing

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Analysis of synthetic aperture radar (SAR) images in L-band of short-period internal waves (IWs), and classification of their radar signatures is presented by means of a polarimetric data set from ALOS-PALSAR mission. We choose the polarimetric feature named standard deviation of the co-polarized phase difference (CPD) to identify fundamental differences in SAR signatures of internal waves, and divided them into three different classes, according to their backscattered modulation depths and morphology as well as the std CPD, namely: double-signed, single-negative, and single-positive signatures, for IW normalized image transects that display, respectively, signatures in the form of bright/dark, dark, and bright bands that correspond to positive/negative, negative, or positive variations of radar backscatter. These radar power types of signatures have a counterpart in the std CPD normalized transects, and in this paper we discuss those correlations and decorrelations. We focus in the single-negative type of signature, that is dark bands on gray background, and show that the std CPD is greatly enhanced over the troughs and rear slopes of those IWs. It is suggested that such behavior is consistent with the presence of surface slicks owing to enhanced surfactant concentration. Furthermore, those single-negative SAR signatures appear at locations where and when biological productivity is enhanced. It is found that the modulation depths associated to the std CPD is higher than the one associated to the HH-polarized radar backscatter for single-negative signatures propagating in the range direction, while the reverse occurs for the other types of signatures.

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Analysis on the Effects of SAR Imaging Parameters and Environmental Conditions on the Standard Deviation of the Co-Polarized Phase Difference Measured over Sea Surface

Abstract: This study aimed at analyzing the effect of Synthetic Aperture Radar (SAR) imaging parameters and environmental conditions on the standard deviation of the co-polarized phase difference (sjC ) evaluated over sea surface[...]

Cited by 14 publications

References 43 publications

The Impact of System Noise in Polarimetric SAR Imagery on Oil Spill Observations

The Impact of System Noise in Polarimetric SAR Imagery on Oil Spill Observations

Marine Oil Slick Detection Based on Multi-Polarimetric Features Matching Method Using Polarimetric Synthetic Aperture Radar Data

Internal Wave Dark-Band Signatures in ALOS-PALSAR Imagery Revealed by the Standard Deviation of the Co-Polarized Phase Difference

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