Multi-scale information extraction from high resolution remote sensing imagery and region partition methods based on GMRF-SVM

$61.20 plus tax (Refund Policy)

Buy Article:

Abstract:

This paper proposes the work flow of multi-scale information extraction from high resolution remote sensing images based on features: rough classification - parcel unit extraction (subtle segmentation) - expression of features - intelligent illation - information extraction or target recognition. This paper then analyses its theoretical and practical significance for information extraction from enormous amounts of data on a large scale. Based on the spectrum and texture of images, this paper presents a region partition method for high resolution remote sensing images based on Gaussian Markov Random Field (GMRF)-Support Vector Machine (SVM), that is the image classification based on GMRF-SVM. This method integrates the advantages of GMRF-based texture classification and SVM-based pattern recognition with small samples and makes it convenient to utilize a priori knowledge. Finally, the paper reports tests on Ikonos images. The experimental results show that the method used here is superior to GMRF-based segmentation in terms of both the time expenditure and processing effect. In addition, it is actually meaningful for the stage of information extraction and target recognition.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160500258974

Affiliations: 1: The State Key Laboratory of Resources and Environmental Information System, IGSNRR, CAS, Beijing, PR China,The Institute of Remote Sensing Application, CAS, Beijing 100101, PR China 2: The State Key Laboratory of Resources and Environmental Information System, IGSNRR, CAS, Beijing, PR China 3: The Institute of Remote Sensing Application, CAS, Beijing 100101, PR China

Publication date: January 1, 2007

More about this publication?
Related content

Share Content

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
ingentaconnect 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