Volume XXXIX-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 237-242, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-237-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 237-242, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-237-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 Jul 2012

31 Jul 2012

A JOINT PIXEL AND REGION BASED MULTISCALE MARKOV RANDOM FIELD FOR IMAGE CLASSIFICATION

T. Mei, L. Zheng, and S. Zhong T. Mei et al.
  • School of Electronic Information, Wuhan University, Wuhan, China

Keywords: Image segmentation, Region based MRF, Pixel based MRF, High resolution image, Multiscale analysis, Watershed transform

Abstract. MRF model is recognized as one of efficient tools for image classification. However, traditional MRF model prove to be limited for high resolution image classification. This paper presents a joint pixel and region based multi-scale MRF model for high resolution image classification. Based on initial image segmentation, the region shape information is integrated into MRF model to consider the pixel and region information simultaneously. The region shaped information is used to complement spectral signature for alleviating spectral signature ambiguity of different classes. The paper describes the unified multi-scale MRF model and classification algorithm. The qualitative and quantitative comparison with traditional MRF model demonstrates that the proposed method can improve the classification performance for regular shaped objects in high resolution image.