Skip to main content

Linear feature extraction using adaptive least-squares template matching and a scalable slope edge model

Buy Article:

$55.00 plus tax (Refund Policy)

This paper presents a linear feature extraction method. Least squares template matching (LSTM) is adopted as the computational tool to fit the linear features with a scalable slope edge (SSE) model, which is based on an explicit function to define the blurred edge profile. In the SSE model, the magnitude of the grey gradient and the edge scale can be described by three parameters; additionally, the edge position can be obtained strictly by the 'zero crossing' location of the profile model. In our method the edge templates are locally and adaptively generated by estimating the three parameters via fitting the image patches with the model, accordingly the linear feature can be positioned with high accuracy by using LSTM. We derived the computational models to rectify straight line and spline curve features and tested those algorithms using the synthetic and real remotely sensed images. The experiments using synthetic images show that the method can position the linear features with the mean geometric error of pixel location of less than one pixel in certain noise levels. Examples of semiautomatic extraction of buildings and linear objects from real imagery are also given and demonstrate the potential of the method.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Affiliations: 1: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, PR China 2: Department of Geography, Faculty of Environmental Studies, University of Waterloo, Waterloo, Ontario, Canada

Publication date: 2009-01-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more