Segmentation of High-resolution Remotely Sensed Imagery Based on Phase Congruency

Segmentation of high-resolution remotely sensed imagery constructs the base of object recognition and object-oriented classification.Performance of watershed transform relies on the algorithm of gradient extraction from the original image.Phase congruency is introduced as a new methodology to detect gradient features from IKONOS Pan imagery.This model postulates that features are perceived at points in an image where the Fourier components are maximally in phase and that the type of features depends on the value of the phase.The multi-scale gradient images are obtained by applying Phase congruency model to the images with Log Gabor wavelet filters over 5 scales and 6 orientations.To restrain the over segmentation of watershed transform,Phase congruency gradient should be marked before the segmentation.But the classical method of marking with foreground and background is proved not suitable for high-resolution remotely sensed imagery.Then a new watershed transform algorithm based on foreground marking and gradient reconstruction is demonstrated.Feature extraction and segmentation are implemented from three types of objects selected from the IKONOS Pan imagery of Nanjing,i.e.paddy,workshop and house images.The results show that Phase congruency is better than Canny detector for the watershed based segmentation.