Hierarchical Segmentation of Multitemporal RADARSAT-2 SAR Data Using Stationary Wavelet Transform and Algebraic Multigrid Method

The objective of this paper is to develop a new effective method for hierarchical segmentation of multitemporal ultrafine-beam synthetic aperture radar (SAR) data in urban areas. Multitemporal RADARSAT-2 ultrafine-beam highresolution horizontal transmit and horizontal receive-Synthetic Aperture Radar (HH-SAR) images acquired in the rural-urban fringe of the Greater Toronto Area during the summer of 2008 are selected for this research. Stationary wavelet transform (SWT) and algebraic multigrid (AMG) method are proposed for segmentation of SAR data. SWT is applied for decomposition of multitemporal SAR images in image preprocessing. The hierarchical and matrix-based AMG method is applied for segmentation. A pyramid of fine-to-coarse grids is constructed by iteration of selecting representative pixels and calculating the interpolation matrix between a fine-level grid and a coarse-level grid. When the pyramid is completed, segments are determined by a top-down scanning based on the interpolation matrices. The AMG techniques provide a complete hierarchical segmentation of SAR data. The experimental results show that our method produces higher accuracy than eCognition.