CFAR detection of extended objects in high-resolution SAR images

Bisceglie, M. Di; Galdi, C.

doi:10.1109/tgrs.2004.843190

Cited by 115 publications

(49 citation statements)

References 18 publications

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“…This step guarantees that the brightest target pixels are detected out. Secondly, we set a threshold 2 T ( 2 T is smaller than 1 T ) and a distance threshold 0 d . Pixels whose gray level is greater than 2 T and where distance from any one central pixel is smaller than 0 d are denoted as target pixels.…”

Section: Extract Rois Using Dual Threshold Segmentation Techniquementioning

confidence: 99%

“…Secondly, we set a threshold 2 T ( 2 T is smaller than 1 T ) and a distance threshold 0 d . Pixels whose gray level is greater than 2 T and where distance from any one central pixel is smaller than 0 d are denoted as target pixels. Consequently, the clutter pixels with high gray level and long distance from central target pixels would not be detected out as target pixels.…”

Section: Extract Rois Using Dual Threshold Segmentation Techniquementioning

confidence: 99%

“…To accelerate the speed of ATR, the detection algorithms must exhibit high detection efficiency. In the past, many detection algorithms for SAR imagery have been presented, for example, the constant false alarm rate (CFAR) detectors [2][3][4] based on contrast or feature brightness; the multiresolution detector [5] using the difference in fluctuation behavior of phase and amplitude from man-made objects and natural clutter under different resolution; the two-feature EF/CFAR detector [6] utilizing both the contrast and extended fractal features. Up to now, target detection for SAR imagery is still an active area of research.…”

Section: Introductionmentioning

confidence: 99%

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Fast algorithm for maneuvering target detection in SAR imagery based on gridding and fusion of texture features

Zhan

Fu-qing

2011

Geo-spatial Information Science

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Designing detection algorithms with high efficiency for Synthetic Aperture Radar (SAR) imagery is essential for the operator SAR Automatic Target Recognition (ATR) system. This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms, and introduces the gridding and fusion idea of different texture features to realize fast target detection. It first grids the original SAR imagery, yielding a set of grids to be classified into clutter grids and target grids, and then calculates the texture features in each grid. By fusing the calculation results, the target grids containing potential maneuvering targets are determined. The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest. The fused texture features, including local statistics features and Gray-Level Co-occurrence Matrix (GLCM), are investigated. The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast detection algorithms using real SAR data. The results obtained from the experiments indicate the promising practical application value of our study.

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Section: Extract Rois Using Dual Threshold Segmentation Techniquementioning

confidence: 99%

Section: Extract Rois Using Dual Threshold Segmentation Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast algorithm for maneuvering target detection in SAR imagery based on gridding and fusion of texture features

Zhan

Fu-qing

2011

Geo-spatial Information Science

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“…1. 1) Global censoring: for multiple target situation in reference cells, global censoring is conducted based on the histogram of entire image [4]. 2) Detection threshold: for given value of PFA, adaptive threshold is computed from the estimated parameters of G 0 distribution.…”

Section: Proposed Cfar Algorithmmentioning

confidence: 99%

Local cell-averaging fast CFAR for multi-target detection in high-resolution SAR images

Jung

Yang

Kwag

2009

2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar

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A local cell-averaging fast CFAR scheme is proposed for multi-target ship detection. The proposed algorithm is based on global censoring scheme to avoid multitarget situation in reference cell. Fast CFAR scheme is employed to reduce computational load. Local average in the vicinity of test cell can remove small sized false alarms so that additional binary clustering is not required for final decision. The test results using real SAR images show good performance in multitarget situation and heterogeneous environment.

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“…The early one-parameter CFAR algorithm uses one parameter to characterize the distribution model. More realistic twoparameter CFAR uses two-parameter distribution models to characterize clutter, such as Weibull distribution [20] and K-distribution [21]. It is assumed that target pixels obey a certain distribution and pixels in the reference window are used to estimate the parameters of the distribution model.…”

Section: Introductionmentioning

confidence: 99%

Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach

et al. 2014

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The human visual system (HVS) possesses a remarkable ability of real-time complex scene analysis despite the limited neuronal hardware available for such tasks. The HVS successfully overcomes the problem of information bottleneck by selecting potential regions of interest and reducing the amount of data transmitted to high-level visual processing. On the other hand, many man-made systems are also confronted with the same problem yet fail to achieve satisfactory performance. Among these, the synthetic aperture radar-based automatic target recognition (SAR-ATR) system is a typical one, where the traditional detection algorithm employed is termed the constant false alarm rate (CFAR). It is known to exhibit a low probability of detection (PD) and consumes too much time. The visual attention model (VAM) is a computational model, which aims to imitate the HVS in predicting where humans will look. The application of VAM to the SAR-ATR system could thus help solve the problem of effective real-time processing of complex large amounts of data. In this paper, we propose a new vehicle target detection algorithm for SAR images based on the VAM. The algorithm modifies the well-known Itti model according to the requirements of target detection in SAR images. The modified Itti model locates salient regions in SAR images and following top-down processing reduces false alarms by using prior knowledge. Real SAR data are used to demonstrate the validity and effectiveness of the proposed algorithm, which is also benchmarked against the traditional CFAR algorithm. Simulation results show comparatively improved performance in terms of PD, number of false alarms and computing time.

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CFAR detection of extended objects in high-resolution SAR images

Cited by 115 publications

References 18 publications

Fast algorithm for maneuvering target detection in SAR imagery based on gridding and fusion of texture features

Fast algorithm for maneuvering target detection in SAR imagery based on gridding and fusion of texture features

Local cell-averaging fast CFAR for multi-target detection in high-resolution SAR images

Visual Attention Model Based Vehicle Target Detection in Synthetic Aperture Radar Images: A Novel Approach

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