The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 169–173, 2013
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 169–173, 2013

  24 Sep 2013

24 Sep 2013


M. A. Fazel1, S. Homayouni2, and J. Amini1 M. A. Fazel et al.
  • 1Dept. of Geomatics Engineering, College of Engineering, University of Tehran, Iran
  • 2Dept. of Geography, University of Ottawa, Canada

Keywords: Change Detection, Synthetic Aperture Radar, Polarimetric SAR, Agricultural lands monitoring, Kernel-based c-means, multi-temporal data analysis

Abstract. Unsupervised change detection of agricultural lands in seasonal and annual periods is necessary for farming activities and yield estimation. Polarimetric Synthetic Aperture Radar (PolSAR) data due to their special characteristics are a powerful source to study temporal behaviour of land cover types. PolSAR data allows building up the powerful observations sensitive to the shape, orientation and dielectric properties of scatterers and allows the development of physical models for identification and separation of scattering mechanisms occurring inside the same region of observed lands. In this paper an unsupervised kernel-based method is introduced for agricultural change detection by PolSAR data. This method works by transforming data into higher dimensional space by kernel functions and clustering them in this space. Kernel based c-means clustering algorithm is employed to separate the changes classes from the no-changes. This method is a non-linear algorithm which considers the contextual information of observations. Using the kernel functions helps to make the non-linear features more separable in a linear space. In addition, use of eigenvectors' parameters as a polarimetric target decomposition technique helps us to consider and benefit physical properties of targets in the PolSAR change detection. Using kernel based c-means clustering with proper initialization of the algorithm makes this approach lead to great results in change detection paradigm.