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

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Kefeng Ji ◽  
Xiangwei Xing ◽  
Huanxin Zou ◽  
Jixiang Sun
Keyword(s):  
Detection Method ◽  
Detection Threshold ◽  
Breaking Waves ◽  
Probability Of Detection ◽  
Masking Effect ◽  
The Novel ◽  
Constant False Alarm Rate ◽  
Detection Scheme ◽  
Ship Detection ◽  
Sar Imagery

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.

A waterborne salient ship detection method on SAR imagery

2015 ◽  
Vol 58 (8) ◽  
pp. 1-3 ◽  
Author(s):  
Long Ma ◽  
Liang Chen ◽  
XueJing Zhang ◽  
He Chen ◽  
Nouman Qadeer Soomro
Keyword(s):  
Detection Method ◽  
Ship Detection ◽  
Sar Imagery

scholarly journals Range-Doppler Based CFAR Ship Detection with Automatic Training Data Selection

2019 ◽  
Vol 11 (11) ◽  
pp. 1270 ◽  
Author(s):  
Sushil Kumar Joshi ◽  
Stefan V. Baumgartner ◽  
Andre B. C. da Silva ◽  
Gerhard Krieger
Keyword(s):  
Real Time ◽  
Data Extraction ◽  
Detection Threshold ◽  
Extraction Procedure ◽  
Radar Data ◽  
Distribution Functions ◽  
Training Data ◽  
Detection Methods ◽  
Constant False Alarm Rate ◽  
Ship Detection

Ship detection is an essential maritime security requirement. Current state-of-the-art synthetic aperture radar (SAR) based ship detection methods employ fully focused images. The time-consuming processing efforts required to generate these images make them generally unsuitable for real time applications. This paper proposes a novel real time oriented ship detection strategy applicable to range-compressed (RC) radar data acquired by an airborne radar sensor during linear, circular and arbitrary flight tracks. A constant false alarm rate (CFAR) detection threshold is computed in the range-Doppler domain using suitable distribution functions. Detection in range-Doppler has the advantage that principally even small ships with a low radar cross section (RCS) can be detected if they are moving fast enough so that the ship signals are shifted to the exo-clutter region. In order to determine a robust threshold, the ocean statistics have to be described accurately. Bright target peaks in the background ocean data bias the statistics and lead to an erroneous threshold. Therefore, an automatic ocean training data extraction procedure is proposed in the paper. It includes (1) a novel target pre-detection module that removes the bright peaks from the data already in time domain, (2) clutter normalization in the Doppler domain using the remaining samples, (3) ocean statistics estimation and (4) threshold computation. Various sea clutter models are investigated and analyzed in the paper for finding the most suitable models for the RC data. The robustness and applicability of the proposed method is validated using real linearly and circularly acquired radar data from DLR’s (Deutsches Zentrum für Luft- und Raumfahrt) airborne F-SAR system.

scholarly journals Multi-Spectral Ship Detection Using Optical, Hyperspectral, and Microwave SAR Remote Sensing Data in Coastal Regions

2018 ◽  
Vol 10 (11) ◽  
pp. 4064 ◽  
Author(s):  
Kyung-Ae Park ◽  
Jae-Jin Park ◽  
Jae-Cheol Jang ◽  
Ji-Hyun Lee ◽  
Sangwoo Oh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Satellite Images ◽  
Coastal Region ◽  
Probability Of Detection ◽  
Constant False Alarm Rate ◽  
Coastal Regions ◽  
Ship Detection ◽  
Radar Images ◽  
Sar Remote Sensing

The necessity of efficient monitoring of ships in coastal regions has been increasing over time. Multi-satellite observations make it possible to effectively monitor vessels. This study presents the results of ship detection methodology, applied to optical, hyperspectral, and microwave satellite images in the seas around the Korean Peninsula. Spectral matching algorithms are used to detect ships using hyperspectral images with hundreds of spectral channels and investigate the similarity between the spectra and in-situ measurements. In the case of SAR (Synthetic Aperture Radar) images, the Constant False Alarm Rate (CFAR) algorithm is used to discriminate the vessels from the backscattering coefficients of Sentinel-1B SAR and ALOS-2 PALSAR2 images. Validation results exhibited that the locations of the satellite-detected vessels showed good agreement with real-time location data within the Sentinel-1B coverage in the Korean coastal region. This study presented the probability of detection values of optical and SAR-based ship detection and discussed potential causes of the errors. This study also suggested a possibility for real-time operational use of vessel detection from multi-satellite images based on optical, hyperspectral, and SAR remote sensing, particularly in the inaccessible coastal regions off North Korea, for comprehensive coastal management and sustainability.

A New Automatic Ship Detection Method Using $L$-Band Polarimetric SAR Imagery

2014 ◽  
Vol 7 (4) ◽  
pp. 1383-1393 ◽  
Author(s):  
Jujie Wei ◽  
Pingxiang Li ◽  
Jie Yang ◽  
Jixian Zhang ◽  
Fengkai Lang
Keyword(s):  
Detection Method ◽  
Polarimetric Sar ◽  
Ship Detection ◽  
Sar Imagery

scholarly journals Incoherent Integration Detection Method of Airborne Phased Array Radar in a Multipath Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yili Hu ◽  
Yongbo Zhao ◽  
Sheng Chen
Keyword(s):  
Phased Array ◽  
Detection Method ◽  
Detection Threshold ◽  
Constant False Alarm Rate ◽  
Detection Model ◽  
Cfar Detection ◽  
Phased Array Radar ◽  
Multipath Signal ◽  
Multipath Environment ◽  
Airborne Phased Array Radar

Airborne phased array radar (PAR) suffers from multipath problems when flying over a calm sea surface. The existence of a multipath phenomenon will cause the electromagnetic echo of the same target to be reflected back to the airborne PAR from two paths, namely, direct path (DP) and multipath. Compared with the ground-based radar, the target echo received by airborne PAR in the multipath environment has two important characteristics: one is that the DP signal and the multipath signal exist in different range bins, and the other is that the radar cross section (RCS) in the DP direction may be smaller than that in the multipath direction. Considering these two characteristics, this paper first proposes a target pairing algorithm for matching the DP range and multipath range of the same target in signal detection, and then, combined with the cell-averaging constant false alarm rate (CA-CFAR) detection model, an incoherent integration detection method for airborne PAR in the multipath environment is proposed. In the target pairing process, the geometric structure relationship of the airborne PAR model can be fully utilized. After a successful target pairing process, the energy of the multipath signal will be incoherently accumulated into the corresponding DP range bin, so as to improve the probability of DP range bin data passing the detection threshold. In essence, the proposed method makes full use of multipath energy to improve the detection capability of airborne PAR in the multipath environment. Finally, the detection probability of the proposed method is given, and the detection performance is analyzed.

scholarly journals An Adaptive Ship Detection Scheme for Spaceborne SAR Imagery

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1345 ◽  
Author(s):  
Xiangguang Leng ◽  
Kefeng Ji ◽  
Shilin Zhou ◽  
Xiangwei Xing ◽  
Huanxin Zou
Keyword(s):  
Detection Scheme ◽  
Ship Detection ◽  
Sar Imagery

scholarly journals Adaptive Ship Detection for Single-Look Complex SAR Images Based on SVWIE-Noncircularity Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3293
Author(s):  
Yu-Huan Zhao ◽  
Peng Liu
Keyword(s):  
Detection Method ◽  
Adaptive Threshold ◽  
Detection Task ◽  
Synthetic Aperture ◽  
Small Islands ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Ship Detection ◽  
Clutter Reduction ◽  
Variance Weighted Information Entropy

In this paper, we present an adaptive ship detection method for single-look complex synthetic aperture radar (SAR) images. First, noncircularity is analyzed and adopted in ship detection task; besides, similarity variance weighted information entropy (SVWIE) is proposed for clutter reduction and target enhancement. According to the analysis of scattering of SVWIE and noncircularity, SVWIE-noncircularity (SN) decomposition is developed. Based on the decomposition, two components, the high-noncircularity SVWIE amplitude (h) and the low-noncircularity SVWIE amplitude (l), are obtained. We demonstrate that ships and clutter in SAR images are different for h detector and h detector can be effectively used for ship detection. Finally, to extract ships from the background, the generalized Gamma distribution (G Γ D) is used to fit h statistics of clutter and the constant false alarm rate (CFAR) is utilized to choose an adaptive threshold. The performance of the proposed method is demonstrated on HH polarization of Alos-2 images. Experimental results show that the proposed method can accurately detect ships in complex background, i.e., ships are close to small islands or with strong noise.

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

2019 ◽  
Vol 11 (6) ◽  
pp. 620 ◽  
Author(s):  
Jun Wang ◽  
Tong Zheng ◽  
Peng Lei ◽  
Xiao Bai
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Detection Method ◽  
Regions Of Interest ◽  
The Other ◽  
Synthetic Aperture ◽  
Constant False Alarm Rate ◽  
Sar Imaging ◽  
Detection Stage ◽  
Sar Imagery

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.

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