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

Authors:
J. Luo;D. Ming;Z. Shen;M. Wang;H. Sheng
Affiliations:
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;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;The State Key Laboratory of Resources and Environmental Information System, IGSNRR, CAS, Beijing, PR China;The State Key Laboratory of Resources and Environmental Information System, IGSNRR, CAS, Beijing, PR China
Venue:
International Journal of Remote Sensing
Year:
2007

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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.