Linear features adaptive extraction from remote sensing image based on beamlet transform

Xiaoming Mei; Ruiqing Niu; Liang-pei Zhang; Ping-xiang Li

doi:10.1117/12.712993

28 October 2006 Linear features adaptive extraction from remote sensing image based on beamlet transform

Xiaoming Mei, Ruiqing Niu, Liang-pei Zhang, Ping-xiang Li

Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64190U (2006) https://doi.org/10.1117/12.712993
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China

Abstract

Extraction of linear features is a classical problem in Remote Sensing image processing. In the last twenty years, it is still difficult to extract linear features embedded in extremely high noise or when the SNR (signal to noise) is low. In this paper, an adaptive algorithm based on beamlet transform is proposed to extract linear features from remote sensing image, which can detect lines with any orientation, location and length, the parameter can be adaptively determined by histogram of beamlet energy function distribution to avoid subjective setting. The experimental results show that the method proposed extract linear features accurately even from high noise remote sensing image and has a better performance. It can be suited to remote sensing images processing and in practice it has surprisingly powerful and apparently unprecedented capabilities.

Citation Download Citation

Xiaoming Mei, Ruiqing Niu, Liang-pei Zhang, and Ping-xiang Li "Linear features adaptive extraction from remote sensing image based on beamlet transform", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64190U (28 October 2006); https://doi.org/10.1117/12.712993

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