基于改进Wedgelet变换的SAR图像边缘检测

针对SAR图像边缘检测中,传统算法很难同时兼顾噪声抑制和对边缘完整准确定位的缺点,利用多尺度Wedgelet变换能够有效检测线目标的特点,提出了一种新的Wedgelet变换的代价函数,增强了其抑噪能力,同时选择了适当的分解尺度,在没有降低逼近图像质量的情况下提高了变换速度.基于此变换,对SAR图像进行自适应的边缘检测.实验结果表明该方法有效克服了斑点噪声的影响,对SAR图像的边缘检测是可行、有效的.

SAR IMAGE EDGE DETECTION BASED ON IMPROVED WEDGELET TRANSFORM

For SAR images, traditional edge detection methods can hardly extract edges since it is very difficult to balance the noise suppression and the integrity of edges and the veracity of edge position at the same time. However, Wedgelet, which is a multiscale geometric analysis tool, has an advantage of catching linear features. Aiming at further reducing speckle noise in SAR images, a modified cost function of Wedgelet transform was proposed. Besides, proper transform scales were selected in order to highly improve the transform speed without affecting the quality of the approximation. Based on the improved Wedgelet transform, edge detection was preformed. Experimental results show that the proposed method can suppress speckle noise in SAR images successfully and extract edges from SAR images effectively.