一种基于CFAR检测和密度聚类的ISAR图像预处理方法

目标逆合成孔径雷达(ISAR)像通常受各种噪声的影响，这些噪声使ISAR图像质量下降，严重影响了后续的特征提取和目标识别应用。提高图像质量，减少噪声的干扰成为ISAR目标识别应用中的重要步骤。提出了一种基于恒虚警检测和密度聚类的方法抑制ISAR像的斑点干扰和横条纹干扰，在保证干扰抑制效果的同时相比于传统方法可以更有效地保留目标中的细节信息。提取了图像面积、长度、多普勒扩展作为ISAR 识别特征矢量，外场实测数据实验表明，提出的预处理方法有效地抑制了图像中的干扰成分，保留了更多图像细节，有效地提高ISAR识别特征的稳定性。

Abstract

Inverse Synthetic Aperture Radar(ISAR) images are usually affected by various noises which degrade the quality of ISAR images and seriously affect the subsequent feature extraction and target recognition applications. Improving the image quality and reducing noise interference have become important steps in the application of target recognition. A method based on Constant False-Alarm Rate (CFAR) detection and density clustering is proposed to suppress speckle interference and transverse fringe interference in ISAR images. Compared with traditional methods, this method can effectively preserve the details of the target while ensuring the effect of interference suppression. The image area, length and Doppler spread are extracted as ISAR recognition feature vectors. Field experiments show that the proposed method effectively suppresses the interference components in the image, retains more image details, and effectively improves the stability of ISAR recognition features.

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