.png){kind=icon}
.png){kind=icon}
Return
A CFAR Target-Detection Method Based on Superpixel Statistical Modeling
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2020.3006033.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The constant false-alarm-rate (CFAR) target detection is an important research direction for synthetic aperture radar (SAR) image application. The traditional pixel-level CFAR method has great shortcomings in eliminating the false-alarm targets and keeping the complete information of the targets. Thus, the superpixel-level CFAR has become an important topic in research in recent years. However, the current superpixel-level CFAR methods have not considered or built a superpixel clutter-distribution model. Therefore, an improved CFAR based on superpixel modeling is proposed in this letter. A superpixel-level compound Gamma distribution was built to describe the clutter statistical model, which can obtain a more accurate fitting than the pixel-level Gamma distribution. The experiments on the SAR images verified that the proposed method can effectively suppress the influence of the background clutter to reduce the false alarms and can keep the complete shape information of the targets. As a result, the proposed method outperforms the traditional pixel-level CFAR and the current superpixel-level CFAR methods.
Keywords:
Government
Analytical models
Petroleum
Data models
Batteries
Automobiles
Industries
Constant false-alarm rate (CFAR)
synthetic aperture radar (SAR)
statistical models
superpixel modeling
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K
Organization
No organization information available