Two-Parameter CFAR Ship Detection Algorithm Based on Rayleigh Distribution in SAR Images
Synthetic Aperture Radar (SAR) is an active type of microwave remote sensing. Using the microwave imaging system, remote sensing monitoring of the land and global ocean can be done in any weather conditions around the clock. Detection of SAR image targets is one of the main needs of radar image interpretation applications. In this paper, an improved two-parameter CFAR algorithm based on Rayleigh distribution and morphological processing is proposed to perform ship detection and recognition in high resolution SAR images. Through simulation experiments, comprehensive study of the two algorithms for high resolution SAR image target detection is achieved. Finally, the results of ship detection experiments are compared and analyzed, and the effects of detection are evaluated according to the Rayleigh distribution model and algorithms.
Code (1)
Tasks
2D Object DetectionImage SegmentationSAR Ship DetectionSimilar Papers 제목 키워드 기반
Wilcoxon Nonparametric CFAR Scheme for Ship Detection in SAR Image
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, ar…
A Novel Radar Constant False Alarm Rate Detection Algorithm Based on VAMP Deep Unfolding
The combination of deep unfolding with vector approximate message passing (VAMP) algorithm, results in faster convergence and higher sparse recovery accuracy than traditional compressive sensing approaches. However, deep…
Compressive SensingUnfolding Target Detection with State Space Model
Target detection is a fundamental task in radar sensing, serving as the precursor to any further processing for various applications. Numerous detection algorithms have been proposed. Classical methods based on signal pr…
modelParameter Convergence Detector Based on VAMP Deep Unfolding: A Novel Radar Constant False Alarm Rate Detection Algorithm
The sub-Nyquist radar framework exploits the sparsity of signals, which effectively alleviates the pressure on system storage and transmission bandwidth. Compressed sensing (CS) algorithms, such as the VAMP algorithm, ar…
compressed sensingCFARNet: Learning-Based High-Resolution Multi-Target Detection for Rainbow Beam Radar
Millimeter-wave (mmWave) OFDM radar equipped with rainbow beamforming, enabled by joint phase-time arrays (JPTAs), provides wide-angle coverage and is well-suited for fast real-time target detection and tracking. However…
Computational Efficiency