中文摘要：

目的 边缘检测是有效利用遥感数据开展地物目标自动识别的重要步骤.高分辨率遥感图像地物类型复杂,细节信息过于丰富,使得基于相位一致的边缘检测结果中存在过多的噪声与伪边缘.为此提出了一种结合相位一致与全变差模型的高分辨率遥感图像边缘检测方法.方法 根据相位一致原理,应用Log Gabor构造的2维相位一致模型,引入全变差去噪模型对基于相位一致的边缘强度图进行改进.结果 借助有界变差空间对图像光滑性的约束,实现了高分辨率遥感图像噪声去除与伪边缘抑制,利用改进后的相位一致边缘强度图可有效检测高分辨率遥感图像的边缘.结论 实验结果表明,与相位一致模型、Canny算法相比,该方法能消除了高分辨率遥感图像中同类地物内部细节特征形成的噪声,抑制相位一致边缘检测结果中的伪边缘,突出地物的真实边缘,并能正确地提取地物目标的整体轮廓信息,有助于后续地物目标的自动识别.

英文摘要：

Objective Edge detection from remote sensing data is an important step of automatic target recognition. However, objects in high-resolution remote sensing images are complex, and the detail information in high spa- tial resolution remote sensing images is often too rich; consequently there is too much noise or pseudo edge in the result of edge detection based on the phase congruency model. Combining phase congruency with total variation model, an approach of edge detection for high-resolution remote sensing image is proposed. Method First, according to the principle of phase congruency, a two-dimensional phase congruency model is constructed using Log Gabor. Second, an edges response mapfrom high-resolution remote sensing image based on a phase congruency model is improved by a total variation model. Result Then high resolution remote sensing image noise removal and pseudo edges inhibition are achieved by space of bounded var- iation restrictions on the image smoothness. Additionally, edges from high-resolution remote sensing images are detected by the improved edges response map based on phase congruency. Conclusion Compared with methods based on the phase congruency model and the Canny algorithm, the experiment results show that proposed approach could eliminate noise pro- duced by the internal details characteristics within the similar objects from high resolution remote sensing image, suppress pseudo edges produced by the phase congruency model, protrude real edges of targets in high resolution remote sensing ima- ges, and could correctly extract the target contour information. Therefore, the provided approach can extract edges from high-resolution remote sensing images effectively, and is therefore helpful for subsequent automatic target recognition.