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 spatial 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. First

according to the principle of phase congruency

a two-dimensional phase congruency model is constructed using Log Gabor.Second

an edges response map from high-resolution remote sensing image based on a phase congruency model is improved by a total variation model. Then high resolution remote sensing image noise removal and pseudo edges inhibition are achieved by space of bounded variation 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. Compared with methods based on the phase congruency model and the Canny algorithm

the experiment results show that proposed approach could eliminate noise produced 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 images

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.