Segmentation of high resolution remote sensing image based on hierarchically multiscale object-oriented Markov random fields model

Abstract

A new segmentation method is proposed for high resolution remote sensing image. In the high-resolution remote sensing image, there is mass of data to be processed, and land objects exhibits strongly hierarchical and multiscale characters. In order to overcome the disadvantages of pixel-based hierarchical MRF model directly used on high-resolution remote sensed images, a hierarchically multiscale object-oriented MRF model (HMSOMRF) is proposed for image segmentation. The proposed method is made up of two blocks: (1)Mean-Shift algorithm is employed to obtain multiscale segmentation results, which can form the hierarchical structure according to the correspondence of different objects in different scale, and the hierarchically multiscale object adjacent tree (HMOAT) can be easily defined. (2)the calculation of the spectral, textural, and shape features of each node, the hierarchical MRF model can be easily defined on the HMOAT for the segmentation of high-resolution remote sensed images. Finally, two high-resolution remote sensed image data sets, GeoEye and IKONOS, are used to testify the performance of MFOMRF. And the experimental results have shown the superiority of this method to the pixel-based hierarchical MRF segmentation method both on the effectively and accuracy, which implies it is suitable for the segmentation of high-resolution remote sensed images.

DOI: 10.1109/ICSDM.2011.5969060

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Cite this paper

@article{Hong2011SegmentationOH, title={Segmentation of high resolution remote sensing image based on hierarchically multiscale object-oriented Markov random fields model}, author={Liang Hong and Zhaozhong Gao and Xianchun Pan and Kun Yang}, journal={Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services}, year={2011}, pages={343-347} }