Stochastic model-based processing for detection of small targets in non-Gaussian natural imagery

Chapple, Philip B.; Bertilone, D.C.; Caprari, Robert S.; Newsam, G.N.

doi:10.1109/83.913590

Cited by 18 publications

(15 citation statements)

References 14 publications

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“…There are several other possibilities (see for instance Chapple et al [2]) but the list has been limited here. A more comprehensive SAR imagery database than is currently available, with good ground-truth, is needed in order to contrast and compare subtle differences between similar detectors.…”

Section: Detection Algorithmsmentioning

confidence: 99%

Comparison of ship detectors for polarimetric SAR imagery

Crisp

Keevers

2010

Oceans'10 Ieee Sydney

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This paper examines the benefits of using fully polarimetric SAR imagery for ship detection. There are three related aspects to the work. First, RadarSat-2 quad-pol stripmap imagery is used to compare the performance of various ship detection algorithms/methods. The comparison is based on the use of ROC curves. Second, circular spotlight SAR imagery is used to examine how detector performance varies with the azimuth angle. Owing to the small number of available ship samples, this analysis has limitations. Third, again using the circular spotlight SAR imagery, the variation, with respect to azimuth angle, of the elements of the polarimetric covariance matrix of both the ship and the ocean are analysed. The results are as follows. Using the stripmap imagery, it is found that exploiting both polarimetry and spatial correlations greatly improves detection performance. Using the circular spotlight imagery, it is found that detection performance is variable with respect to azimuth angle and trends are hard to discern. Moreover, the performance of the Pauli double bounce detector is generally poor. It is conjectured that ship pitching motion degrades the polarimetric backscatter response in the circular spotlight imagery. Finally, the covariance matrix analysis confirms known trends for the ocean backscatter. The implication of these trends is that the polarimetric covariance matrix is sensitive to changes in azimuth angle and hence detectors based on them are also. The data indicate the extent to which this is the case.

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Section: Detection Algorithmsmentioning

confidence: 99%

Comparison of ship detectors for polarimetric SAR imagery

Crisp

Keevers

2010

Oceans'10 Ieee Sydney

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“…Other examples illustrating the a contrario decision methodology can be found among the literature about detection of low resolution targets over a cluttered background (see for example (Chapple et al, 2001) or (Watson and Watson, 1996)). A probabilistic model for the background over which the sought objects lie is first built, then objects are detected if they are not likely to be generated by the background.…”

Section: Contribution To Matching Decision and Related Workmentioning

confidence: 99%

An A Contrario Decision Method for Shape Element Recognition

Musé

Sur

Cao

et al. 2006

Int J Comput Vision

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Shape recognition is the field of computer vision which addresses the problem of finding out whether a query shape lies or not in a shape database, up to a certain invariance. Most shape recognition methods simply sort shapes from the database along some (dis-)similarity measure to the query shape. Their main weakness is the decision stage, which should aim at giving a clear-cut answer to the question: "do these two shapes look alike?" In this article, the proposed solution consists in bounding the number of false correspondences of the query shape among the database shapes, ensuring that the obtained matches are not likely to occur "by chance". As an application, one can decide with a parameterless method whether any two digital images share some shapes or not.

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“…For this reason, ships often appear as bright spots in SAR intensity images. This peculiarity has led to the development of several algorithms aimed at detecting bright points on a darker background [3,[15][16][17][18][19][22][23][24][25][26][27][28][29][30]. The backscattering from the sea is strongly influenced by the sea state, and in some situations, it can be extraordinarily bright, covering the return from small vessels.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection with Spectral Analysis of Synthetic Aperture Radar: A Comparison of New and Well-Known Algorithms

et al. 2015

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The surveillance of maritime areas with remote sensing is vital for security reasons, as well as for the protection of the environment. Satellite-borne synthetic aperture radar (SAR) offers large-scale surveillance, which is not reliant on solar illumination and is rather independent of weather conditions. The main feature of vessels in SAR images is a higher backscattering compared to the sea background. This peculiarity has led to the development of several ship detectors focused on identifying anomalies in the intensity of SAR images. More recently, different approaches relying on the information kept in the spectrum of a single-look complex (SLC) SAR image were proposed. This paper is focused on two main issues. Firstly, two recently developed sub-look detectors are applied for the first time to ship detection. Secondly, new and well-known ship detection algorithms are compared in order to understand which has the best performance under certain circumstances and if the sub-look analysis improves ship detection. The comparison is done on real SAR data exploiting diversity in frequency and polarization. Specifically, the employed data consist of six RADARSAT-2 fine quad-polacquisitions over the North Sea, five TerraSAR-X HH/VV dual-polarimetric data-takes, also over the North Sea, and one ALOS-PALSAR quad-polarimetric dataset over Tokyo Bay. Simultaneously to the Remote Sens. 2015, 7 5417 SAR images, validation data were collected, which include the automatic identification system (AIS) position of ships and wind speeds. The results of the analysis show that the performance of the different sub-look algorithms considered here is strongly dependent on polarization, frequency and resolution. Interestingly, these sub-look detectors are able to outperform the classical SAR intensity detector when the sea state is particularly high, leading to a strong clutter contribution. It was also observed that there are situations where the performance improvement thanks to the sub-look analysis is not so noticeable.

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Stochastic model-based processing for detection of small targets in non-Gaussian natural imagery

Cited by 18 publications

References 14 publications

Comparison of ship detectors for polarimetric SAR imagery

Comparison of ship detectors for polarimetric SAR imagery

An A Contrario Decision Method for Shape Element Recognition

Ship Detection with Spectral Analysis of Synthetic Aperture Radar: A Comparison of New and Well-Known Algorithms

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