A noise removal algorithm for LIDAR intensity image based on orientation gradient and weighted threshold

Shengwang Zhang; Youchan Wan

doi:10.1117/12.725449

11 January 2007 A noise removal algorithm for LIDAR intensity image based on orientation gradient and weighted threshold

Shengwang Zhang, Youchan Wan

Author Affiliations +

Proceedings Volume 6279, 27th International Congress on High-Speed Photography and Photonics; 62792R (2007) https://doi.org/10.1117/12.725449
Event: 27th International congress on High-Speed Photography and Photonics, 2006, Xi'an, China

Abstract

In this paper, an algorithm of LIDAR intensity image noise removal based on orientation gradient and weighted threshold is proposed on the analysis of LIDAR imaging mechanism. It detects and removes the noise in LIDAR intensity image based on the orientation gradient of the ground got from LIDAR distance information, during which weighted threshold is adopted to calculate the threshold automatically. Experiments prove that this filter can detect and remove the noise effectively and preserve the edge information of Intensity Image better.

Citation Download Citation

Shengwang Zhang and Youchan Wan "A noise removal algorithm for LIDAR intensity image based on orientation gradient and weighted threshold", Proc. SPIE 6279, 27th International Congress on High-Speed Photography and Photonics, 62792R (11 January 2007); https://doi.org/10.1117/12.725449

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available