Extracting linear features from SAR images using CGVF Snake model and Beamlet transform

Extracting Linear Features from Microwave Images (SAR) using CGVF Snake Model and Beamlet Transform is proposed in this paper. Microwave images are independent of climate. In certain durations these images are affected by noise. So noise is removed in this work. For CGVF Snake model, edges are detected and GVF field is produced followed by Snake is initialized and then two external constraint forces are developed. The first one will points on the snake and determine the basic shape of a snake. The second one generating the curves is smooth and grows in a correct direction. For each iteration, the snake is deformed in edges and removes the discontinuities for extracting the linear features. For beamlet Transform, initially the SAR image is enhanced by applying the Non-uniform Background elimination method followed by Beamlet Transform based algorithm was applied. This algorithm recursively partitions the image into sub-squares to build a beamlet dictionary to perform the transform followed by discontinuities are linked to extract the linear features. The results of feature extraction from microwave images using CGVF Snake model and Beamlet Transform to increase the correctness and quality of satellite Mapping.