Linear Features Extraction From Remote Sensing Image Based on Wedgelet Decomposition

Abstract

Linear feature extraction is an important problem for remote sensing image processing, and it is very difficult to extract those linear features embedded in strong noise or when the SNR (signal to noise) is low like the complicated environment of remote sensing image. In this paper, an algorithm based on wedgelet decomposition is proposed to extract linear features from remote sensing image. Firstly, beamlets can be generated by recursive dyadic partitioning, vertex marking and connecting in different scales, and beamlet transform is implemented as one important parameter to generate edge map of linear feature. Secondly, each dyadic square is split into two wedgelet segments, and wedgelet decomposition is implemented as the other important parameter to generate edge map of linear feature. The propose method can detect lines with any orientation, location and length in different scales. Experimental results show that the proposed method can extract linear features accurately from remote sensing image. It can be suited to remote sensing image processing and in practice it has surprisingly powerful and apparently unprecedented capabilities.