An Adaptive and Fast CFAR Algorithm Based on Multithreading for Ship Detection in SAR Image

Wang, Ruifu; Li, Jie; Hao, Y.; Li, Jiagui

doi:10.14257/ijhit.2017.10.8.05

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…The multi-level CFAR detection algorithm based on multithreading (Wang et al 2017) combines global detection and local detection; in addition, multi-threaded parallel computing technology is utilized. The specific process can be described as follows: First, global detection is performed on the whole SAR image, and the histogram statistics is used.…”

Section: Final Detection Resultsmentioning

confidence: 99%

Study on the Combined Application of CFAR and Deep Learning in Ship Detection

Wang

Li²,

Ya-ping

et al. 2018

J Indian Soc Remote Sens

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To maintain national socio-economic development and maritime rights and interests, it is necessary to obtain the space location information of various ships. Therefore, it is important to detect the locations of ships accurately and rapidly. At present, ship detection is mainly carried out by combining satellite remote sensing imaging with constant false alarm rate (CFAR) detection. However, with the rapid development of satellite remote sensing technology, remote sensing data have gradually begun to show the characteristics of ''big data''; additionally, the accuracy and speed of ship detection can be improved by analysing big data, such as by deep learning. Thus, a ship detection algorithm that combines CFAR and CNN is proposed based on the CFAR global detection algorithm and image recognition with the CNN model. Compared with the multi-level CFAR algorithm that is based on multithreading, the algorithm in this paper is more suitable for application to ship detection systems.

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Section: Final Detection Resultsmentioning

confidence: 99%