Feature Extraction of Satellite Images Using 2D Phase Congruency Measure
J. Krishna Chaithanya, T. Rama Shri · 2013
All the satellite images, which are going to be used in the present work, are going to be processed in the computer vision, for which the existing researchers are interested to analyze the synthetic images by feature extraction. These images contain many types of features. Indeed, the features are classified in 1-D feature such as step, roof and 2-D features such as corners, edges, and blocks. The satellite images present a great variety of features due to the trouble what returns their treatment is little delicate. In this we present a method for edge segmentation of satellite images based on 2-D Phase Congruency (PC) model. The proposed approach is composed by two steps: The contextual nonlinear smoothing algorithm (CNLS) is used to smooth the input images. Then, the 2D stretched Gabor filter (S-G filter) based on proposed angular variation is developed in order to avoid the multiple responses. Noise reduction is necessary for us to do image processing and image interpretation so as to acquire useful information that we want. Because the working status of image transmitter is influenced by varied of factors, such as the environment of image acquired, different noises can be dealt with in different ways and affected by notable striping. The Gray Value Substitution and Wavelet Transformation are satisfactory in stripped noise reduction.