A Novel Statistical Texture Feature for SAR Building Damage Assessment in Different Polarization Modes

Abstract

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 G 0 distribution of SAR image and used to distinguish collapse buildings and intact buildings. First, the G0para 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-/compactpolarization, 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.

A Wave Texture Difference Method for Rainfall Detection Using X-Band Marine Radar

“…In [29], the incident angle (IA) dependence of seven commonly used grey level cooccurrence matrix (GLCM) texture features is investigated, and the results are included in the Gaussian incident angle (GIA) classifier for evaluation. In [30,31], a new method of the image texture statistics and analysis is proposed by using different texture features of undamaged buildings and collapsed buildings. The proposed method is used to extract the building damage information for effective emergency decision-making.…”

A Detection Method for Collapsed Buildings Combining Post-Earthquake High-Resolution Optical and Synthetic Aperture Radar Images

“…Even so, the diversity of collapsed buildings and complexity of postearthquake scenarios lead to more prominent problems of different objects with the same spectra and same object with different spectra, which requires establishing more discriminative classification models. Furthermore, the lack of elevation information, as the direct evidence to determine whether buildings collapse or not, is still the main challenge in practical application of such methods [15]. (3) Methods combining elevation data: Based on remote sensing images, elevation information provided by elevation data, such as LiDAR and digital elevation model (DEM), is used in such methods as a strong basis for determining whether buildings collapse or not [16][17][18].…”

Comparative Analysis of Edge Information and Polarization on SAR-to-Optical Translation Based on Conditional Generative Adversarial Networks

“…Therefore, it is also important to evaluate the difference in echo intensity between cross-polarization and co-polarization and determine the effect of this difference on the SAR-to-optical translation process. Furthermore, the extent to which dual-polarization can improve the recognition degree from that of the single-polarization mode remains unknown [11,[63][64][65].…”