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

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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.

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

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