Texture features are important characteristics in distinguishing collapsed buildings and intact buildings. However, texture features currently used in synthetic aperture radar (SAR) building damage assessment are extracted following the methods of optical images directly, which do not consider the statistical feature of speckles and limit the accuracy improving. Therefore, a statistical texture feature—G0-para—was proposed to reflect the homogeneity of buildings in complex urban areas after a disaster. The G0-para is arising from the G0 distribution of SAR image and used to distinguish collapse buildings and intact buildings. First, the G0-para is unified to satisfy different polarization data—single-/dual-/quad-/compact-polarization. Second, the distinguishing ability of G0-para is under comparison in single-/dual-/quad-/compact-polarization, through the receiver operating characteristic (ROC) curve and the area under the ROC curve analysis. Then, collapsed buildings with RADARSAT-2 and ALOS-1 data are evaluated, selecting the optimal combinations of each mode and comparing with the preferable existing texture features. The results show that the statistical texture parameter—G0-para—is better than the variance of gray-level histogram and the contrast of gray level co-occurrence matrix in distinguishing intact buildings and collapsed ones and G0-para can be applied to single-/dual-/quad-/compact-polarimetric SAR data. For experimental data, VV and HH in single polarization, VH/VV and HH/HV in dual polarization, and hybrid mode in compact polarization are recommended when the best quad polarization is unavailable.

Segmentation techniques play an important role in understanding high-resolution polarimetric synthetic aperture radar (PolSAR) images. PolSAR image segmentation is widely used as a preprocessing step for subsequent classification, scene interpretation and extraction of surface parameters. However, speckle noise and rich spatial features of heterogeneous regions lead to blurred boundaries of high-resolution PolSAR image segmentation. A novel segmentation algorithm is proposed in this study in order to address the problem and to obtain accurate and precise segmentation results. This method integrates statistical features into a fractal net evolution algorithm (FNEA) framework, and incorporates polarimetric features into a simple linear iterative clustering (SLIC) superpixel generation algorithm. First, spectral heterogeneity in the traditional FNEA is substituted by the G⁰ distribution statistical heterogeneity in order to combine the shape and statistical features of PolSAR data. The statistical heterogeneity between two adjacent image objects is measured using a log likelihood function. Second, a modified SLIC algorithm is utilized to generate compact superpixels as the initial samples for the G⁰ statistical model, which substitutes the polarimetric distance of the Pauli RGB composition for the CIELAB color distance. The segmentation results were obtained by weighting the G⁰ statistical feature and the shape features, based on the FNEA framework. The validity and applicability of the proposed method was verified with extensive experiments on simulated data and three real-world high-resolution PolSAR images from airborne multi-look ESAR, spaceborne single-look RADARSAT-2, and multi-look TerraSAR-X data sets. The experimental results indicate that the proposed method obtains more accurate and precise segmentation results than the other methods for high-resolution PolSAR images.

Keywords: Synthetic Aperture Radar (SAR), Building Collapse Information, Texture, G0 Distribution, Texture Parameter Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G⁰ distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.