Abstract
A novel segmentation algorithm for polarimetric synthetic aperture radar (PolSAR) images is proposed in this paper. The method is composed of two essential components: a merging order and a merging predicate. The similarity measured by the complex-kind Hotelling–Lawley trace (HLT) statistic is used to decide the merging order. The merging predicate is determined by the scattering characteristics and the revised Wishart distance between adjacent pixels, which can greatly improve the performance in speckle suppression and detail preservation. A postprocessing step is applied to obtain a satisfactory result after the merging operation. The decomposition and merging processes are iteratively executed until the termination criterion is met. The superiority of the proposed method was verified with experiments on two RADARSAT-2 PolSAR images and a Gaofen-3 PolSAR image, which demonstrated that the proposed method can obtain more accurate segmentation results and shows a better performance in speckle suppression and detail preservation than the other algorithms.
Highlights
Synthetic aperture radar (SAR) systems conduct remote sensing and global Earth monitoring under the illumination of radar beams, which offer a day-and-night and all-weather monitoring capability compared with optical sensors
This shows that image segmentation can be viewed as a likelihood approximation problem, and its adaptation for the segmentation of homogeneous and textured scenes has been shown by experiments
For polarimetric synthetic aperture radar (PolSAR), the scattering characteristics are inherent in the data
Summary
Synthetic aperture radar (SAR) systems conduct remote sensing and global Earth monitoring under the illumination of radar beams, which offer a day-and-night and all-weather monitoring capability compared with optical sensors. In [21], a split-merge test was derived for the segmentation of multifrequency PolSAR images following the maximum-likelihood approach This approach is especially useful in the extraction of information from urban areas that are characterized by the presence of different spectral and polarimetric characteristics. The segmentation method proposed in [22] is equivalent to region merging based on a likelihood-ratio test with an optimized merging order, where the least different pair of neighboring regions is merged in each step. This shows that image segmentation can be viewed as a likelihood approximation problem, and its adaptation for the segmentation of homogeneous and textured scenes has been shown by experiments.
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