Robustness of a Generalized Gamma CFAR ship detector applied to TerraSAR-X and Sentinel-1 images

Martín-de-Nicolás, Jaime; Jarabo-Amores, P.; del-Rey-Maestre, Nerea; Gómez-del-Hoyo, Pedro; Bárcena-Humanes, J. L.

doi:10.1109/eurocon.2015.7313775

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…In some studies, it is shown that the whole pixels in the sub-image can be used to estimate model parameters, because the number of ship pixels is much smaller than that of ocean pixels and hence the influence of ship pixels on the statistical properties of sea clutter is negligible [7]. In other studies, the brightest pixels can be removed [21] [37] by suitable CFAR detectors, such as the order statistic CFAR (OS-CFAR), the trimmed mean CFAR (TM CFAR), the censored mean-level detector (CMLD) and the truncated statistic CFAR (TS-CFAR), etc [21]. Additionally outlier removal is usually done by data ranking or censoring with different restrictions [21].…”

Section: Real Data Validation: Testing On Smaller Areasmentioning

confidence: 99%

CFAR Ship Detection in Polarimetric Synthetic Aperture Radar Images Based on Whitening Filter

Liu

Zhang

Gao

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Polarimetric Whitening Filter (PWF) can be used to filter Polarimetric Synthetic Aperture (PolSAR) images to improve the contrast between ships and sea clutter background. For this reason, the output of the filter can be used to detect ships. This work deals with the setting of the threshold over PolSAR images filtered by the PWF. Two parameter-constant false alarm rate (2P-CFAR) is a common detection method used on whitened polarimetric images. It assumes that the probability density function (PDF) of the filtered image intensity is characterized by a lognormal distribution. However, this assumption does not always hold. In this paper, we propose a systemic analytical framework for CFAR algorithms based on PWF or multi-look PWF (MPWF). The framework covers the entire log-cumulants space in terms of the textural distributions in the product model, including the constant, Gamma, Inverse Gamma, Fisher, Beta, inverse Beta and Generalized Gamma (GΓD) distributions. We derive the analytical forms of the PDF for each of the textural distributions and the probability of false alarm (PFA). Finally, the threshold is derived by fixing the false alarm rate (FAR). Experimental results using both the simulated and real data demonstrate that the derived expressions and CFAR algorithms are valid and robust.

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Section: Real Data Validation: Testing On Smaller Areasmentioning

confidence: 99%

CFAR Ship Detection in Polarimetric Synthetic Aperture Radar Images Based on Whitening Filter

Liu

Zhang

Gao

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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“…To accelerate the speed of image processing, a local block detection is proposed, which consists in dividing the whole image into blocks [36] [53].…”

Section: A Local Detection Designmentioning

confidence: 99%

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Liu

Yang

Marino

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Constant false alarm rate (CFAR) algorithms using local training window are widely used for ship detection with synthetic aperture radar (SAR) imagery. However, when the density of targets is high, such as in busy shipping lines and crowded harbors, the background statistics may be contaminated by the presence of nearby targets in the training window. Recently a robust CFAR detector based on truncated statistics (TS) was proposed. However, the truncation of data in the format of polarimetric covariance matrices is much more complicated with respect to the truncation of intensity (single polarization) data. In this paper a polarimetric truncated statistic CFAR (PWF-TS-CFAR) is proposed to estimate the background parameters accurately in the contaminated sea clutter for Pol SAR imagery. The CFAR detector uses polarimetric whitening filter (PWF) to turn the multidimensional problem to a one dimensional case. It uses the truncation to exclude possible statistically interfering outliers and uses TS to model the remaining background samples. The algorithm does not require a prior knowledge of the interfering targets and it is performed iteratively and adaptively to derive better estimates of the polarimetric covariance matrix (although this is computationally expensive). The PWF-TS-CFAR detector provides accurate background clutter modeling, a stable false alarm property, and improves detection performance in high-target-density situations. RadarSat2 data are used to verify our derivations and the results are in line with the theory.

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“…procedure should be performed prior to the estimation of the wind speed. This study used the adaptive threshold method to detect ship pixels in the SAR images based on the peculiar scattering characteristics (Friedman et al 2001;Chen et al 2005;Martín-de-Nicolás et al 2015). The adaptive threshold method is one of the most widely used methods for target detection in the SAR images, since neither high computational efficiency nor additional data information are necessary for its execution.…”

Section: Ship Detection and Speckle Noise Reductionmentioning

confidence: 99%

Characteristics of environmental factors contributing to ALOS-2 PALSAR wind speed errors

Jang

Park

Isoguchi

2020

International Journal of Remote Sensing

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Environmental factors affecting the errors in wind speed estimates were investigated using the Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2 PALSAR-2) data for the period from 1 November 2014 to 30 November 2017. In total, 45 ALOS-2 PALSAR-2 Stripmap Fine mode images with horizontal dual-polarization were collected, and the wind speeds were calculated using the L-band Geophysical Model Function (GMF) in 2009. Validation of the SAR-derived wind speeds to in-situ wind, converted to the 10-m neutral wind, resulted in the root-meansquare error of 2.11 ms −1 , bias error of −1.16 m s −1 , and standard deviation of 1.78 m s −1. Investigation of the wind speed errors revealed the contributions of diverse oceanic environmental factors such as the ship and its side-lobe effect that is negligible at a distance of more than 200% of the ship radius, v-shaped ship wakes are accompanied by the Kelvin waves and turbulent wakes caused by ship movement. The sand ridge, formed due to the interaction of shallow bathymetry and tidal currents, contributed to the wind speed errors of more than 50%. The internal waves caused prominent wind speed errors of more than 100%. The atmospheric gravity waves, generated by the orographic effect, caused wind speed fluctuations up to 13.4 m s −1. This study addressed the importance of understanding the factors that affect the normalized radar crosssection of the SAR image in order to correctly retrieve wind speeds from SAR data.

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Robustness of a Generalized Gamma CFAR ship detector applied to TerraSAR-X and Sentinel-1 images

Cited by 12 publications

References 20 publications

CFAR Ship Detection in Polarimetric Synthetic Aperture Radar Images Based on Whitening Filter

CFAR Ship Detection in Polarimetric Synthetic Aperture Radar Images Based on Whitening Filter

Robust CFAR Detector Based on Truncated Statistics for Polarimetric Synthetic Aperture Radar

Characteristics of environmental factors contributing to ALOS-2 PALSAR wind speed errors

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