Combined contextual classification method for large scale land covering based on multi-resolution satellite data

QiongHua Wang; WenJu He; WeiDong Sun

doi:10.1117/12.749490

14 November 2007 Combined contextual classification method for large scale land covering based on multi-resolution satellite data

QiongHua Wang, WenJu He, WeiDong Sun

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Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 679003 (2007) https://doi.org/10.1117/12.749490
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

For multi-resolution land covering classification, many researches have focused on selecting and integrating appropriate feature information from different spatial resolution data of the same area. However, when extending to large scale problems, it is no surprise that low resolution data has worse performance, and high resolution data with wide coverage area has more limitations. To solve this problem, a novel framework is presented which compounds multiple spatial resolution data at arithmetic level without the limitation of full-scale multi-resolution data. The framework allows integrating conditional random fields (CRFs) with "real" likelihood distribution. Discrete feature-likelihood mapping is proposed to represent multi-to-single spatial correspondence. By considering spatial contextual information between pixels, CRFs based classifier offers a robust and accurate framework. Our experiments show that the proposed method can greatly improve the accuracy for large scale land covering classification applications.

Citation Download Citation

QiongHua Wang, WenJu He, and WeiDong Sun "Combined contextual classification method for large scale land covering based on multi-resolution satellite data", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 679003 (14 November 2007); https://doi.org/10.1117/12.749490

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KEYWORDS

Spatial resolution

Image resolution

Satellites

Associative arrays

Lithium

Data modeling

Statistical analysis

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