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2009, IEEE Transactions on Geoscience and Remote Sensing
Journal of Applied Remote Sensing (Open Access)
Target detection in synthetic aperture radar imagery: a state-of-the-art survey2013 •
Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidean distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery.
Algorithms for Synthetic Aperture Radar Imagery XIX
Region based target detection approach for synthetic aperture radar images and its parallel implementation2012 •
2015 Sensor Signal Processing for Defence (SSPD)
A Location Scale Based CFAR Detection Framework for FOPEN SAR Images2015 •
1998 •
2000 •
ABSTRACT Correlation filters are attractive for synthetic aperture radar (SAR) automatic target recognition (ATR) because of their shift invariance and potential for distortion-tolerant pattern recognition. In particular, the maximum average correlation height (MACH) filter exhibits better distortion tolerance than other linear correlation filters. Despite its attractive features, it has been shown that the MACH filter relies perhaps too heavily on the average training image leading to poor clutter rejection performance. To improve the clutter rejection performance, we have introduced the extended MACH (EMACH) filter. We have shown that this new filter is better at rejecting clutter images while retaining the distortion tolerance feature of the original MACH filter. In this paper, we introduce a method to decompose the EMACH filter to further improve its performance. The paper describes the theory of this method and shows its potential advantages. Test results of this method using the public domain MSTAR data base are shown.
Microwave and Optical Technology Letters
Clutter reduction in synthetic aperture radar images with statistical modeling: An application to MSTAR data2008 •
2011 •
Classification of SAR images has extensive applications in national economy and military field. The SAR images, totally different from optical images, i.e. photographs, and their visual interpretation is not straightforward. Therefore, there is need to devise novel strategies for classification of SAR images. In this paper a novel methodology has been carried out to classify SAR images by using the Adaptive Thresholding Technique. This technique composes of three main processes: firstly, selecting training samples i.e. mean value for every region in the SAR image. Secondly, training these samples using 5X5 window, and obtain variance of every region. Finally, the classification of SAR image with respect to group frequency i.e. generated automatically.
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European Radar Conference, 2005. EURAD 2005.
A Novel Approach for the Automatic Detection of Punctual Isolated Targets in a Noisy Background in SAR Imagery2005 •
IEEE Geoscience and Remote Sensing Letters
On a Novel Approach Using MLCC and CFAR for the Improvement of Ship Detection by Synthetic Aperture Radar2010 •
2009 •
Iee Proceedings-radar Sonar and Navigation
Segmentation-based technique for ship detection in SAR images2001 •
The Record of the 1993 IEEE National Radar Conference
Artificial intelligence applications to constant false alarm rate (CFAR) processing1993 •
Anais do XXXIX Simpósio Brasileiro de Telecomunicações e Processamento de Sinais
Target Detection Method for Intensity VHF Wavelength-Resolution SAR ImagesImage and Vision Computing
Genetic algorithm based feature selection for target detection in SAR images2003 •
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
AIS-Based Evaluation of Target Detectors and SAR Sensors Characteristics for Maritime Surveillance2014 •
AEU - International Journal of Electronics and Communications
Automatic censoring CFAR detector for heterogeneous environments2014 •
2008 IEEE/OES US/EU-Baltic International Symposium
Ship detection over single-look complex SAR images2008 •
Proceedings of the First International Conference on Telecommunications and Remote Sensing
A New Method for Moving Target Detection in Sar Imagery2012 •
Proceedings of the 2001 IEEE Radar Conference (Cat. No.01CH37200)
Ship detection in SAR images: a segmentation-based approach2001 •
IEEE Transactions on Aerospace and Electronic Systems
Multi-Model CFAR Detection in FOliage PENetrating SAR Images2017 •
ISPRS International Journal of Geo-Information
Mixture Statistical Distribution Based Multiple Component Model for Target Detection in High Resolution SAR Imagery2017 •
IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)
Target detection and analysis based on spectral analysis of a SAR image:a simulation approach2003 •
Cognitive Computation
Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images2016 •
1996 •
IEEE Transactions on Geoscience and Remote Sensing
CFAR edge detector for polarimetric SAR images2003 •
2018 •
2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07
SAR Processor based on a CFAR Signal or Interference Subspace Detector Matched to Man Made Target Detection in a Forest2007 •
International Journal of Engineering Research and Technology (IJERT)
IJERT-An Approach of Segmentation Technique of SAR Images using Adaptive Thresholding Technique2014 •
IEEE Transactions on Geoscience and Remote Sensing
A statistical and geometrical edge detector for SAR images1988 •
2001 •
1987 •
IEEE Transactions on Image Processing
Target discrimination in synthetic aperture radar using artificial neural networks1998 •
2019 20th International Radar Symposium (IRS)
Automatic CFAR Ship Detection in Single–Channel Range-Compressed Airborne Radar DataTELKOMNIKA (Telecommunication Computing Electronics and Control)
Object Detector on Coastal Surveillance Radar Using Two-Dimensional Order-Statistic Constant-False Alarm Rate Algoritm2015 •
International Conference on Image Processing
A Clutter Rejection Technique for FLIR Imagery Using Region-Based Principal Component Analysis1999 •
2006 IEEE Conference on Radar
Maximum A-Posteriori Adaptive Masking for Clutter Suppression in Automatic Radar Target Recognition2006 •
1995 •