143 research outputs found

    CFAR Edge Detector for Polarimetric SAR Images

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    Abstract—Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector for polarimetric SAR images is presented using a newly developed test statistic in the complex Wishart distribution to test for equality of covariance matrices. The new edge detector can be applied to a wide range of SAR data from single-channel intensity data to multifrequency and/or multitemporal polarimetric SAR data. By simply changing the parameters characterizing the test statistic according to the applied SAR data, constant false-alarm rate detection is always obtained. An adaptive filtering scheme is presented, and the distributions of the detector are verified using simulated polarimetric SAR images. Using SAR data from the Danish airborne polarimetric SAR, EMISAR, it is demonstrated that superior edge detection results are obtained using polarimetric and/or multifrequency data compared to using only intensity data. Index Terms—Complex Wishart distribution, edge detection, polarimetry, synthetic aperture radar (SAR), Wishart likelihoodrati

    Statistical tests for a ship detector based on the Polarimetric Notch Filter

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    Ship detection is an important topic in remote sensing and Synthetic Aperture Radar has a valuable contribution, allowing detection at night time and with almost any weather conditions. Additionally, polarimetry can play a significant role considering its capability to discriminate between different targets. Recently, a new ship detector exploiting polarimetric information was developed, namely the Geometrical Perturbation Polarimetric Notch Filter (GP-PNF). This work is focused on devising two statistical tests for the GP-PNF. The latter allow an automatic and adaptive selection of the detector threshold. Initially, the probability density function (pdf) of the detector is analytically derived. Finally, the Neyman-Pearson (NP) lemma is exploited to set the threshold calculating probabilities using the clutter pdf (i.e. a Constant False Alarm Rate, CFAR) and a likelihood ratio (LR). The goodness of fit of the clutter pdf is tested with four real SAR datasets acquired by the RADARSAT-2 and the TanDEM-X satellites. The former images are quad-polarimetric, while the latter are dual-polarimetric HH/VV. The data are accompanied by the Automatic Identification System (AIS) location of vessels, which facilitates the validation of the detection masks. It can be observed that the pdf's fit the data histograms and they pass the two sample Kolmogorov-Smirnov and χ2 tests

    A Novel Variable Index and Excision CFAR Based Ship Detection Method on SAR Imagery

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    When applying the constant false alarm rate (CFAR) detector to ship detection on synthetic aperture radar (SAR) imagery, multiple interferers such as upwelling, breaking waves, ambiguities, and neighboring ships in a dense traffic area will degrade the probability of detection. In this paper, we propose a novel variable index and excision CFAR (VIE-CFAR) based ship detection method to alleviate the masking effect of multiple interferers. Firstly, we improve the variable index (VI) CFAR with an excision procedure, which censors the multiple interferers from the reference cells. And then, the paper integrates the novel CFAR concept into a ship detection scheme on SAR imagery, which adopts the VIE-CFAR to screen reference cells and the distribution to derive detection threshold. Finally, we analyze the performances of the VIE-CFAR under different environments and validate the proposed method on both ENVISAT and TerraSAR-X SAR data. The results demonstrate that the proposed method outperforms other existing detectors, especially in the presence of multiple interferers

    Polarimetric SAR Change Detection with the Complex Hotelling-Lawley Trace Statistic

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    Accepted manuscript version. Published version at http://dx.doi.org/10.1109/TGRS.2016.2532320.In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling-Lawley trace statistic for measuring the similarity of two covariance matrices. The distribution of the Hotelling-Lawley trace statistic is ap- proximated by a Fisher-Snedecor distribution, which is used to define the significance level of a false alarm rate regulated change detector. Experiments on simulated and real PolSAR data sets demonstrate that the proposed change detection method gives detections rates and error rates that are comparable with the generalized likelihood ratio test

    An Automatic Ship Detection Method Based on Local Gray-Level Gathering Characteristics in SAR Imagery

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    This paper proposes an automatic ship detection method based on gray-level gathering characteristics of synthetic aperture radar (SAR) imagery. The method does not require any prior knowledge about ships and background observation. It uses a novel local gray-level gathering degree (LGGD) to characterize the spatial intensity distribution of SAR image, and then an adaptive-like LGGD thresholding and filtering scheme to detect ship targets. Experiments on real SAR images with varying sea clutter backgrounds and multiple targets situation have been conducted. The performance analysis confirms that the proposed method works well in various circumstances with high detection rate, fast detection speed and perfect shape preservation

    Forcing scale invariance in multipolarization SAR change detection

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    This paper considers the problem of coherent (in the sense that both amplitudes and relative phases of the polarimetric returns are used to construct the decision statistic) multi-polarization SAR change detec- tion starting from the availability of image pairs exhibiting possible power mismatches/miscalibrations. The principle of invariance is used to characterize the class of scale-invariant decision rules which are insensitive to power mismatches and ensure the Constant False Alarm Rate (CFAR) property. A maximal invariant statistic is derived together with the induced maximal invariant in the parameter space which significantly compress the data/parameters domain. A Generalized Likelihood Ratio Test (GLRT) is synthesized both for the cases of two- and three-polarimetric channels. Interestingly, for the two-channel case, it is based on the comparison of the condition number of a data-dependent matrix with a suitable threshold. Some additional invariant decision rules are also proposed. The performance of the considered scale-invariant structures is compared to those from two non- invariant counterparts using both simulated and real radar data. The results highlight the robustness of the proposed method and the performance tradeoff involve

    A Non-Parametric Texture Descriptor for Polarimetric SAR Data with Applications to Supervised Classification

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    The paper describes a novel representation of polarimetric SAR (PolSAR) data that is inherently non-parametric and therefore particularly suited for characterising data in which the commonly adopted hypothesis of Gaussian backscatter is not appropriate. The descriptor is also non-local and can capture image structure in terms of the arrangement of edge-, ridge- and point-like features, to yield a salient characerisation of semi-periodic spatial patterns. The basic approach is based closely on [1] and has been adapted for application to PolSAR data. As an example application, the descriptor is evaluated in the context of supervised classification. The performance is compared with conventional statistical approaches on both simulated and real PolSAR dat

    Evaluation of the Wishart test statistics for polarimetric SAR data

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