Screening Image Features of Collapsed Buildings for Operational and Rapid Remote Sensing Identification

Abstract

The accurate detection of collapsed buildings is of great significance for post-disaster rescue and reconstruction. High-resolution optical images are important data sources for identifying collapsed buildings, and the identification accuracy mainly depends on the features extracted from the images. However, existing research lacks a comprehensive screening and general evaluation of the ability of remote sensing features to detect collapsed buildings, and there is still a considerable gap in the operational process of rapid identification of collapsed buildings in remote sensing. Based on 2630 pairs of building samples distributed in 6 regions worldwide, this study evaluated the ability of 25 remote sensing features (including spectral and spatial features) to detect collapsed buildings and select the most capable ones. Then, we test the application effect of selected features in identifying collapsed buildings on large-scale remote sensing images. Based on the two experiments above, an operational process for rapid identification of collapsed buildings was suggested. The result shows that Homogeneity, Energy, Local Entropy, Local Standard Deviation, and Gradient can effectively and stably distinguish collapsed buildings from non-collapsed buildings (Jeffries-Matusita distances are greater than 1.59 and Transformed Divergences are greater than 1.60) and have high recognition accuracy for collapsed buildings on large-scale remote sensing images (F1-scores are 0.71–0.94). In addition, Contrast, Local Coefficient of Variation, Edge Density, and Global Entropy can also distinguish collapsed buildings from non-collapsed buildings at a normal level (Jeffries-Matusita distances are 1.14–1.28, and Transformed Divergences are 1.24–1.48), while Gradient Orientation Entropy, Fractal Dimension, Local Binary Patterns, Edge, Local Mean, Correlation, Gradient Orientation Standard Deviation, Global Coefficient of Variation, Gabor feature, Local Moran’I, and six spectral features have relatively weak abilities (Jeffries-Matusita distances are less than 0.73, and Transformed Divergences are less than 1.07). The selected remote sensing features can support rapid identification of potential collapsed building areas from post-disaster remote sensing images.