Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 107-110, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-107-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 107-110, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-107-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  24 Sep 2013

24 Sep 2013

SUPERVISED CLASSIFICATION OF POLARIMETRIC SAR IMAGERY USING TEMPORAL AND CONTEXTUAL INFORMATION

A. Dargahi, Y. Maghsoudi, and A. A. Abkar A. Dargahi et al.
  • Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Vali-Asr St., Mirdamad Crossing, P.O. Box 15875-4416, Tehran 19967-15433, Iran

Keywords: Synthetic Aperture Radar (SAR), Polarimetry, Markov Random Field (MRF), Contextual Classification, Statistical Modeling, Forest

Abstract. Using the context as a source of ancillary information in classification process provides a powerful tool to obtain better class discrimination. Modelling context using Markov Random Fields (MRFs) and combining with Bayesian approach, a context-based supervised classification method is proposed. In this framework, to have a full use of the statistical a priori knowledge of the data, the spatial relation of the neighbouring pixels was used. The proposed context-based algorithm combines a Gaussian-based wishart distribution of PolSAR images with temporal and contextual information. This combination was done through the Bayes decision theory: the class-conditional probability density function and the prior probability are modelled by the wishart distribution and the MRF model. Given the complexity and similarity of classes, in order to enhance the class separation, simultaneously two PolSAR images from two different seasons (leaf-on and leaf-off) were used. According to the achieved results, the maximum improvement in the overall accuracy of classification using WMRF (Combining Wishart and MRF) compared to the wishart classifier when the leaf-on image was used. The highest accuracy obtained was when using the combined datasets. In this case, the overall accuracy of the wishart and WMRF methods were 72.66% and 78.95% respectively.