A new CFAR ship target detection method in SAR imagery

Abstract

Many ship target detection methods have been developed since it was verified that ship could be imaged with the space-based SAR systems. Most developed detection methods mostly emphasized ship detection rate but not computation time. By making use of the advantages of the K-distribution CFAR method and two-parameter CFAR method, a new CFAR ship target detection algorithm was proposed. In that new method, we use the K-distribution CFAR method to calculate a global threshold with a certain false-alarm rate. Then the threshold is applied to the whole SAR imagery to determine the possible ship target pixels, and a binary image is given as the preliminary result. Mathematical morphological filter are used to filter the binary image. After that step, we use the two-parameter CFAR method to detect the ship targets. In the step, the local sliding window only works in the possible ship target pixels of the SAR imagery. That step avoids the statistical calculation of the background pixels, so the method proposed can much improve the processing speed. In order to test the new method, two SAR imagery with different background were used, and the detection result shows that that method can work well in different background circumstances with high detection rate. Moreover, a synchronous ship detection experiment was carried out in Qingdao port in October 28, 2005 to verify the new method and one ENVISAT ASAR imagery was acquired to detect ship targets. It can be concluded from the experiment that the new method not only has high detection rate, but also is time-consuming, and is suitable for the operational ship detection system.

Dynamically adapted ship parameter estimation using TerraSAR-X images

“…Wang et al (2014) exploit the fact that sidelobes are always propagated straight in the azimuth and range directions. Another solution is image erosion on the binary ship mask (Sonka, Hlavac, and Boyle 2007;Ji et al 2010). Both techniques have the ability to diminish the distorting effect of sidelobes.…”

Research of fiber optical faceplate defects segmentation based on improved watershed algorithm

“…The bottom-hat [10] transform is defined as the difference between the closed operation of the image and the original image.The bottom-hat transform can find the valley in the image and highlight the boundary between the connected objects.Its definition is as follows:…”

Adaptive ship detection in SAR images using variance WIE-based method

“…The figure of merit (FoM) in [6] is used as the evaluation index to assess the detection results. The higher values of FoM mean higher detection rate and lower false alarm rate.…”

“…The higher values of FoM mean higher detection rate and lower false alarm rate. FoM can be defined as follows: (6) where N tt counts the accurate detected ship number, N fa counts the false alarm number and N gt counts the real ship target number. Table 1 presents the evaluation indexes of the two methods.…”

“…The direct methods detect ships directly. Of those methods, adaptive threshold way [2], probability neural network (PNN) model method [3] and distributed constant false alarm rate (CFAR) methods [4][5][6] are the generally used methods. The indirect methods firstly detect ship wakes and then seek ships around wakes, which mainly include Radon transform, mathematical morphology and wavelet analysis [7].…”