scholarly journals Target Detection in High-Resolution SAR Image via Iterating Outliers and Recursing Saliency Depth

2021 ◽  
Vol 13 (21) ◽  
pp. 4315
Author(s):  
Zongyong Cui ◽  
Yi Qin ◽  
Yating Zhong ◽  
Zongjie Cao ◽  
Haiyi Yang
Keyword(s):  
High Resolution ◽  
False Alarm ◽  
Target Detection ◽  
Detection Threshold ◽  
Graph Model ◽  
Conditional Entropy ◽  
Image Features ◽  
Feature Model ◽  
Sar Image ◽  
Constant False Alarm Rate

In dealing with the problem of target detection in high-resolution Synthetic Aperture Radar (SAR) images, segmenting before detecting is the most commonly used approach. After the image is segmented by the superpixel method, the segmented area is usually a mixture of target and background, but the existing regional feature model does not take this into account, and cannot accurately reflect the features of the SAR image. Therefore, we propose a target detection method based on iterative outliers and recursive saliency depth. At first, we use the conditional entropy to model the features of the superpixel region, which is more in line with the actual SAR image features. Then, through iterative anomaly detection, we achieve effective background selection and detection threshold design. After that, recursing saliency depth is used to enhance the effective outliers and suppress the background false alarm to realize the correction of superpixel saliency value. Finally, the local graph model is used to optimize the detection results. Compared with Constant False Alarm Rate (CFAR) and Weighted Information Entropy (WIE) methods, the results show that our method has better performance and is more in line with the actual situation.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

scholarly journals Implementation of a Modified Faster R-CNN for Target Detection Technology of Coastal Defense Radar

2021 ◽  
Vol 13 (9) ◽  
pp. 1703
Author(s):  
He Yan ◽  
Chao Chen ◽  
Guodong Jin ◽  
Jindong Zhang ◽  
Xudong Wang ◽  
...  
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Real Data ◽  
Detection Performance ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Data Set ◽  
Detection Technology ◽  
Coastal Defense

The traditional method of constant false-alarm rate detection is based on the assumption of an echo statistical model. The target recognition accuracy rate and the high false-alarm rate under the background of sea clutter and other interferences are very low. Therefore, computer vision technology is widely discussed to improve the detection performance. However, the majority of studies have focused on the synthetic aperture radar because of its high resolution. For the defense radar, the detection performance is not satisfactory because of its low resolution. To this end, we herein propose a novel target detection method for the coastal defense radar based on faster region-based convolutional neural network (Faster R-CNN). The main processing steps are as follows: (1) the Faster R-CNN is selected as the sea-surface target detector because of its high target detection accuracy; (2) a modified Faster R-CNN based on the characteristics of sparsity and small target size in the data set is employed; and (3) soft non-maximum suppression is exploited to eliminate the possible overlapped detection boxes. Furthermore, detailed comparative experiments based on a real data set of coastal defense radar are performed. The mean average precision of the proposed method is improved by 10.86% compared with that of the original Faster R-CNN.

scholarly journals Offshore Platform Extraction Using RadarSat-2 SAR Imagery: A Two-Parameter CFAR Method Based on Maximum Entropy

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 556 ◽  
Author(s):  
Qi Wang ◽  
Jing Zhang ◽  
Fenzhen Su
Keyword(s):  
False Alarm ◽  
Maximum Entropy ◽  
Target Detection ◽  
Detection Threshold ◽  
River Estuary ◽  
Offshore Platform ◽  
Accuracy Evaluation ◽  
Oil And Gas Development ◽  
Offshore Oil ◽  
Two Parameter

The ability to determine the number and location of offshore platforms is of great significance for offshore oil spill monitoring and offshore oil and gas development. Considering the problem that the detection threshold parameters of the two-parameter constant false alarm rate (CFAR) algorithm require manual and repeated adjustment of the during the extraction of offshore platform targets, this paper proposes a two-parameter CFAR target detection method based on maximum entropy based on information entropy theory. First, a series of threshold parameters are obtained using the two-parameter CFAR algorithm for target detection. Then, according to the maximum entropy principle, the optimal threshold is estimated to obtain the target detection results of the possible offshore platform. Finally, the neighborhood analysis method is used to eliminate false alarm targets such as ships, and the final target of the offshore platform is obtained. In this study, we conducted offshore platform extraction experiments and an accuracy evaluation using data from the Pearl River Estuary Basin of the South China Sea. The results show that the proposed method for platform extraction achieves an accuracy rate of 97.5% and obtains the ideal offshore platform distribution information. Thus, the proposed method can objectively obtain the optimal target detection threshold parameters, greatly reduce the influence of subjective parameter setting on the extraction results during the target detection process and effectively extract offshore platform targets.

scholarly journals New matrix constant false alarm rate detectors for radar target detection

2019 ◽  
Vol 2019 (19) ◽  
pp. 5597-5601 ◽  
Author(s):  
Wenjing Zhao ◽  
Deyue Zou ◽  
Wenlong Liu ◽  
Minglu Jin
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Radar Target ◽  
Constant False Alarm Rate

scholarly journals Adjacent Infrared Multitarget Detection Using Robust Background Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Sungho Kim ◽  
Kyung-Tae Kim
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Constant False Alarm Rate ◽  
Cluttered Environments ◽  
Background Estimation ◽  
Cfar Detection

Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).

AN AUTOMATIC EVENT DETECTOR AT THE TONTO FOREST SEISMIC OBSERVATORY

Geophysics ◽  
1974 ◽  
Vol 39 (5) ◽  
pp. 633-643 ◽  
Author(s):  
R. R. Blandford
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Kuril Island ◽  
Detection Threshold ◽  
Maximum Effect ◽  
False Alarms ◽  
Constant False Alarm Rate ◽  
Statistical Bias ◽  
Short Period ◽  
On Line

The on‐line operation of an automatic event detector has been evaluated at the Tonto Forest Observatory short‐period seismic array. For 31 seismometers and one fixed threshold, the 90 percent incremental detection threshold on the Kuril Island beam, centered at Δ=70 degrees, is [Formula: see text] with an experimentally determined false alarm rate of 0.17 per day. This compares favorably with the capabilities of a human operator. Storms in the Kurils significantly affect the distribution of amplitudes of the F-statistic detection trace, and we estimate that most of the false alarms observed at the operating threshold can be traced to the statistical bias introduced by this storm‐generated energy. If the threshold were adjusted to maintain a constant false alarm rate, the maximum effect on the threshold magnitude would be [Formula: see text].

scholarly journals Robust Control for the Detection Threshold of CFAR Process in Cluttered Environments

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3904
Author(s):  
Jeong Hoon Shin ◽  
Youngjin Choi
Keyword(s):  
Robust Control ◽  
False Alarm ◽  
Detection Threshold ◽  
Detection Performance ◽  
Time Interval ◽  
Constant False Alarm Rate ◽  
Cluttered Environments ◽  
Probability Of False Alarm ◽  
Feedback Structure ◽  
Improved Performance

The constant false alarm rate (CFAR) process is essential for target detection in radar systems. Although the detection performance of the CFAR process is normally guaranteed in noise-limited environments, it may be dramatically degraded in clutter-limited environments since the probabilistic characteristics for clutter are unknown. Therefore, sophisticated CFAR processes that suppress the effect of clutter can be used in actual applications. However, these methods have the fundamental limitation of detection performance because there is no feedback structure in terms of the probability of false alarm for determining the detection threshold. This paper presents a robust control scheme for adjusting the detection threshold of the CFAR process while estimating the clutter measurement density (CMD) that uses only the measurement sets over a finite time interval in order to adapt to time-varying cluttered environments, and the probability of target existence with finite measurement sets required for estimating CMD is derived. The improved performance of the proposed method was verified by simulation experiments for heterogeneous situations.

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