The satellite images present a great variety of features due to the trouble what returns their treatment is little delicate. The automated extraction of linear features from remotely sensed imagery has been the subject of extensive research over several decades. Recent studies show promise for extraction of feature information for applications such as updating geographic information systems (GIS). Research has been stimulated by the increase in available imagery in recent years following the launch of several airborne and satellite sensors. 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.
CHAITHANYA, J.KRISHNA and SHRI, T.RAMA
"LINEAR FEATURE EXTRACTION FOR SATELLITE IMAGES USING CNLS (CONTEXTUAL NONLINEAR SMOOTHING) ALGORITHM,"
International Journal of Image Processing and Vision Science: Vol. 2
, Article 2.
Available at: https://www.interscience.in/ijipvs/vol2/iss1/2