Ship Detection With Superpixel-Level Fisher Vector in High-Resolution SAR Images

2020 ◽  
Vol 17 (2) ◽  
pp. 247-251 ◽  
Author(s):  
Huiping Lin ◽  
Hang Chen ◽  
Kan Jin ◽  
Liang Zeng ◽  
Jian Yang
2014 ◽  
Vol 1044-1045 ◽  
pp. 1040-1044
Author(s):  
Qing Ping Wang ◽  
Hong Zhu ◽  
Wei Wei Wu ◽  
Chang Zhu ◽  
Nai Chang Yuan

An improved algorithm for ship detection from the high-resolution synthetic aperture radar (SAR) images is proposed in this paper. In this algorithm, we firstly utilize the image pre-processing step to suppress the speckle noise. Then, the ship ROIs (Region of Interest) are obtained based on MSER (Maximally Stable Extremal Region) method, which enables preliminary extraction of ship candidates. Finally, an improved CFAR (Constant False Alarm Rate) detector is designed for accurate detection with the purpose of accelerating the whole process and decreasing false alarms. The experimental results show that this method can achieve effective ship detection in high-resolution SAR images. The process of ship detection is also accelerated which is in favour of the project realization.


Author(s):  
Ruochen Wu

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.


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