Multilayer CFAR Detection of Ship Targets in Very High Resolution SAR Images

Hou, Biao; Chen, Xingzhong; Jiao, Licheng

doi:10.1109/lgrs.2014.2362955

Cited by 75 publications

(14 citation statements)

References 25 publications

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“…Manual feature-based methods for SAR object detection harness a variety of SAR data characteristics, including polarization, statistical, texture, shape, and size features, to identify objects. For instance, CFAR-based methods use SAR backscatter to separate targets from clutter [14][15][16], while polarization-based methods analyze polarimetric SAR data for target signatures [17][18][19][20]. Geometric feature-based methods rely on dimensions and proportions [21][22][23][24][25], and HOG-based methods focus on local gradients for geometric invariance in detection [26][27][28].…”

Section: Sar Remote Sensing Object Detection Methodsmentioning

confidence: 99%

YOLO-SAD: An Efficient SAR Aircraft Detection Network

Chen,

Shen,

Liang

et al. 2024

Applied Sciences

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Aircraft detection in SAR images of airports remains crucial for continuous ground observation and aviation transportation scheduling in all weather conditions, but low resolution and complex scenes pose unique challenges. Existing methods struggle with accuracy, overlapping detections, and missed targets. We propose You Only Look Once-SAR Aircraft Detector (YOLO-SAD), a novel detector that tackles these issues. YOLO-SAD leverages the Attention-Efficient Layer Aggregation Network-Head (A-ELAN-H) module to prioritize essential features for improved accuracy. Additionally, the SAR Aircraft Detection-Feature Pyramid Network (SAD-FPN) optimizes multi-scale feature fusion, boosting detection speed. Finally, Enhanced Non-Maximum Suppression (EH-NMS) eliminates overlapping detections. On the SAR Aircraft Detection Dataset (SADD), YOLO-SAD achieved 91.9% AP(0.5) and 57.1% AP(0.5:0.95), surpassing the baseline by 2.1% and 1.9%, respectively. Extensive comparisons on SADD further demonstrate YOLO-SAD’s superiority over five state-of-the-art methods in both AP(0.5) and AP(0.5:0.95). The outcomes of further comparative experiments on the SAR-AIRcraft-1.0 dataset confirm the robust generalization capability of YOLO-SAD, demonstrating its potential use in aircraft detection with SAR.

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Section: Sar Remote Sensing Object Detection Methodsmentioning

confidence: 99%

YOLO-SAD: An Efficient SAR Aircraft Detection Network

Chen,

Shen,

Liang

et al. 2024

Applied Sciences

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“…The frontal line extraction algorithm consists of three steps: SO (Smallest of)-CFAR for binary classification [26][27][28][29], morphological image processing [30,31], and maximal cumulative based frontal point extraction [32]. CFAR detectors are adaptable threshold detectors that use various statistical models to detect target returns from the ice shelf against the background clutter, such as sea ice and ocean.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Temporal and Spatial Variability of Fronts on the Amery Ice Shelf Automatically Detected Using Sentinel-1 SAR Data

2021

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The Amery Ice Shelf (AIS) dynamics and mass balance caused by iceberg calving and basal melting are significant in the ocean climate system. Using satellite imagery from Sentinel-1 SAR, we monitored the temporal and spatial variability of the frontal positions on the Amery Ice Shelf, Antarctica, from 2015 to 2021. In this paper, we propose an automatic algorithm based on the SO-CFAR strategy and a profile cumulative method for frontal line extraction. To improve the accuracy of the extracted frontal lines, we developed a framework combining the Constant False Alarm Rate (CFAR) and morphological image-processing strategies. A visual comparison between the proposed algorithm and state-of-the-art algorithm shows that our algorithm is effective in these cases including rifts, icebergs, and crevasses as well as ice-shelf surface structures. We present a detailed analysis of the temporal and spatial variability of fronts on AIS that we find, an advance of the AIS frontal line before the D28 calving event, and a continuous advance after the event. The study reveals that the AIS extent has been advanced at the rate of 1015 m/year. Studies have shown that the frontal location of AIS has continuously expanded. From March 2015 to May 2021, the frontal location of AIS expanded by 6.5 km; while the length of the AIS frontal line is relatively different after the D28 event, the length of the frontal line increased by about 7.5% during 2015 and 2021 (255.03 km increased to 273.5 km). We found a substantial increase in summer advance rates and a decrease in winter advance rates with the seasonal characteristics. We found this variability of the AIS frontal line to be in good agreement with the ice flow velocity.

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“…When the radar resolution increases, the distribution of the SAR clutter deviates from the Gaussian assumption and shows a long tail of the distribution. In order to improve the performance for the ship detection in SAR images, various statistical distributions were used to model the SAR clutter backgrounds [2][3][4][5][6][7][8][9][10][11] , such as K, the alpha-stable distribution, G 0 , log-normal, Gamma, Rayleigh, Rayleigh mixtures distribution, etc. In [2], the simulated annealing method was used to segment the high resolution SAR image into different regions with homogeneous characteristics, and the CA-CFAR based on the K distribution was performed inside each homogeneous region.…”

Section: Introductionmentioning

confidence: 99%

“…In [4], an adaptive and fast CFAR algorithm based on G 0 distribution for the ships detection in SAR image was proposed. In [5], the log-normal distribution was used to model the clutter background in SAR images and a multilayer CFAR detection for ship target in SAR images was proposed, which detects and eliminates the target pixels repeatedly. In order to reduce the influence of the multiple targets on the estimation of the background statistics, the truncated statistics (TS) CFAR method [6] based on Gamma distribution for ship detection in single-look intensity and multi-look intensity SAR data was proposed.…”

Section: Introductionmentioning

confidence: 99%

Wilcoxon Nonparametric CFAR Scheme for Ship Detection in SAR Image

Meng

2024

Preprint

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The parametric constant false alarm rate (CFAR) detection algorithms which are based on various statistical distributions, such as Gaussian, Gamma, Weibull, log-normal, G0 distribution, alpha-stable distribution, etc, are most widely used to detect the ship targets in SAR image at present. However, the clutter background in SAR images is complicated and variable. When the actual clutter background deviates from the assumed statistical distribution, the performance of the parametric CFAR detector will deteriorate. In addition to the parametric CFAR schemes, there is another class of nonparametric CFAR detectors which can maintain a constant false alarm rate for the target detection without the assumption of a known clutter distribution. In this work, the Wilcoxon nonparametric CFAR scheme for ship detection in SAR image is proposed and analyzed, and a closed form of the false alarm rate for the Wilcoxon nonparametric detector to determine the decision threshold is presented. By comparison with several typical parametric CFAR schemes on Radarsat-2, ICEYE-X6 and Gaofen-3 SAR images, the robustness of the Wilcoxon nonparametric detector to maintain a good false alarm performance in different detection backgrounds is revealed, and its detection performance for the weak ship in rough sea surface is improved to some extent. Moreover, the Wilcoxon nonparametric detector can suppress the false alarms resulting from the sidelobes at some degree and its detection speed is fast.

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Multilayer CFAR Detection of Ship Targets in Very High Resolution SAR Images

Cited by 75 publications

References 25 publications

YOLO-SAD: An Efficient SAR Aircraft Detection Network

YOLO-SAD: An Efficient SAR Aircraft Detection Network

Analysis of Temporal and Spatial Variability of Fronts on the Amery Ice Shelf Automatically Detected Using Sentinel-1 SAR Data

Wilcoxon Nonparametric CFAR Scheme for Ship Detection in SAR Image

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