Fuzzy statistical normalization CFAR detector for non-rayleigh data

Xu, Yanwei; Hou, Chaohuan; Yan, Shefeng; Li, Jun; Chen, Hao

doi:10.1109/taes.2014.130683

Cited by 25 publications

(12 citation statements)

References 29 publications

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“…In the non‐homogeneous exponential distribution clutter, Zaimbashi and Norouzi [13] report an automatic dual censoring CA (ADCCA)‐CFAR performing robustly in non‐homogeneous exponential distribution clutter, which requires little prior information about the observation background and can perform robustly. In addition, considering the minority of targets and interferences, defuzzification transforms the fuzzy data into the crisp reverberation data through alpha‐cut (

α

‐cut) [14]. In the spiky Weibull clutter environment, an adaptive censoring ML‐CFAR detector (ACML‐CFAR) [15] is proposed and based on the ODV statistics.…”

Section: Introductionmentioning

confidence: 99%

Constant false alarm rate detector based on sparsity regularisation in multi‐target interfering Weibull clutter

Zhang

et al. 2019

IET Radar, Sonar & Navigation

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α

‐cut) [14]. In the spiky Weibull clutter environment, an adaptive censoring ML‐CFAR detector (ACML‐CFAR) [15] is proposed and based on the ODV statistics.…”

Section: Introductionmentioning

confidence: 99%

Constant false alarm rate detector based on sparsity regularisation in multi‐target interfering Weibull clutter

Zhang

et al. 2019

IET Radar, Sonar & Navigation

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“…The clutter was statistically modelled by the K ‐distribution, and the scale and shape parameters were estimated using the fractional method of moments. Xu et al [25] proposed a fuzzy‐based CFAR detector for non‐homogeneous clutter. The statistical model of the clutter was K ‐distribution, and the method of moments was utilised for the estimation of the shape parameter.…”

Section: Introductionmentioning

confidence: 99%

CFAR detection algorithm for objects in sonar images

Abu

Diamant

2020

IET Radar, Sonar & Navigation

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“…In a large SAR image, target detection can be based on the feature difference between targets and backgrounds. In this process, a minimum region in one target chip containing the whole target can be confirmed [9], and the other part is considered background. Obvious feature differences normally exist between target and background regions, i.e., grayscale, multi-resolution, polarization, phase, etc., which form the basis for the design of many target detection methods.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

et al. 2019

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The ghost phenomenon in synthetic aperture radar (SAR) imaging is primarily caused by azimuth or range ambiguities, which cause difficulties in SAR target detection application. To mitigate this influence, we propose a ship target detection method in spaceborne SAR imagery, using a hierarchical convolutional neural network (H-CNN). Based on the nature of ghost replicas and typical target classes, a two-stage CNN model is built to detect ship targets against sea clutter and the ghost. First, regions of interest (ROIs) were extracted from a large imaged scene during the coarse-detection stage. Unwanted ghost replicas represented major residual interference sources in ROIs, therefore, the other CNN process was executed during the fine-detection stage. Finally, comparative experiments and analyses, using Sentinel-1 SAR data and various assessment criteria, were conducted to validate H-CNN. Our results showed that the proposed method can outperform the conventional constant false-alarm rate technique and CNN-based models.Remote Sens. 2019, 11, 620 2 of 17 recognition [11]. In addition, the conventional constant false alarm rate (CFAR) technique is a typical detection method based on the grayscale feature. However, complicated and cluttered backgrounds severely affect CFAR detection performance [12].In recent years, ship target detection based on deep learning (DL) has been widely studied [13,14], using the typical model of convolutional neural network (CNN) [15]. Liu et al. [16] presented a ship detection method, namely sea-land segmentation-based convolutional neural network (SLS-CNN), which combines a SLS-CNN detector, saliency computation, and corner features. Furthermore, Zhao et al. [17] proposed a spaceborne SAR ship detection algorithm based on low complexity CNN. Some other well-known CNN-based target detection methods include faster region-CNN (Faster R-CNN), you only look once (YOLO) list model, etc. For example, Li et al. [18,19] improved detection performance using Faster R-CNN, to successfully provide a densely connected multi-scale neural network [19]. This method is used to solve multi-scale and multi-scene problems in SAR ship detection. Feature maps are fused by densely connecting different feature map layers, rather than information from single feature maps, which represent top-to-down feature map connections. The R-CNN method is used for target recognition in large scene SAR images [20]. Furthermore, Hamza and Cai used YOLOv2 for ship detection [21], which introduced a multitude of enhancements into the original YOLO model.However, these methods may be no longer effective when ghost replicas exist in an imaged scene. The ghost phenomenon is an intrinsic effect of SAR's ambiguity, both in azimuth and range [22,23]. Range ambiguity occurs when different backscattered echoes-one related to a transmitted pulse and the other due to a previous transmission-temporarily overlap during the receiving operation [24]. On the other hand, azimuth ambiguity is caused by the aliasing of each target's Doppler phase...

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Fuzzy statistical normalization CFAR detector for non-rayleigh data

Cited by 25 publications

References 29 publications

Constant false alarm rate detector based on sparsity regularisation in multi‐target interfering Weibull clutter

Constant false alarm rate detector based on sparsity regularisation in multi‐target interfering Weibull clutter

CFAR detection algorithm for objects in sonar images

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

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