Extraction of linear features based on beamlet transform
Yueze Zhu, Ezzatollah Salari · 2011
Over the past few decades, Synthetic Aperture Radar (SAR) images have been widely used to estimate the various features on the ground. As SAR is a radar system, its images are degraded by noise which limits the application of SAR images. Therefore, extracting features from SAR images with noise is an important issue. The goal of this paper is to develop and implement a beamlet based method to extract the linear features from SAR images with noise and a low signal-to-noise ratio (SNR). The proposed method uses digital image pre-processing techniques to offset the noise and low-contrast problems and to recalculate pixel values. Linear features such as a road network are then extracted by applying the beamlet transform based algorithm. The algorithm recursively partitions the image into sub-squares to build a beamlet dictionary to perform the transform. The complete linear features are then obtained with the post-processing algorithm to link the discontinuities. Experimental results have demonstrated the effectiveness of this method.