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Target detection for SAR images based on beamlet transform

机译：基于小波变换的SAR图像目标检测

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Target detection for SAR images has many important applications; however there is a challenge that inherent speckle noise in SAR images may cause serious interference. Beamlet transform is a multi-scale image analysis method to extract line features in an image with strong anti-noise capacity. In this paper a method based on Beamlet transform is proposed for target detection for SAR images. It takes the advantage of Beamlet transform in feature extraction. Firstly Beamlet transform is applied on a SAR image to obtain Beamlet coefficients,which are then processed by a coefficient filtering algorithm to remove unreal Beamlet features caused by noise. The remained Beamlet features are fed to the BD-RDP (Beamlet-decorated recursive dyadic partition) algorithm for optimization and then clustered by NEC (Nearest Endpoint Clustering) algorithm to detect targets. The experimental results show that this method is able to detect target directly in a SAR image without pre-filtering. Further more, it still works well under the background of strong speckle noise.
机译：SAR图像的目标检测有许多重要的应用。但是，SAR图像中固有的斑点噪声可能会引起严重的干扰，这是一个挑战。子束变换是一种多尺度图像分析方法，用于提取具有强大抗噪能力的图像中的线特征。提出了一种基于小波变换的SAR图像目标检测方法。它在特征提取中利用了Beamlet变换的优势。首先对SAR图像进行Beamlet变换，得到Beamlet系数，然后通过系数滤波算法进行处理，去除噪声引起的不真实Beamlet特征。剩余的Beamlet特征被馈送到BD-RDP（Beamlet装饰的递归二分分割）算法进行优化，然后通过NEC（最近端点聚类）算法进行聚类以检测目标。实验结果表明，该方法无需预先过滤即可直接在SAR图像中检测目标。此外，它在强斑点噪声的背景下仍然可以正常工作。

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