Pre-screener for automatic detection of road damage in SAR imagery via advanced image processing techniques

Synthetic aperture radar (SAR) benefits from persistent imaging capabilities that are not reliant on factors such as weather or time of day. One area that may benefit from readily available imaging capabilities is road damage detection and assessment occurring from disasters such as earthquakes, sinkholes, or mudslides. This work investigates the performance of a pre-screener for an automatic detection system used to identify locations and quantify the severity of road damage present in SAR imagery. The proposed pre-screener is comprised of two components: advanced image processing and classification. Image processing is used to condition the data, removing non-pertinent information from the imagery which helps the classifier achieve better performance. Specifically, we utilize shearlets; these are powerful filters that capture anisotropic features with good localization and high directional sensitivity. Classification is achieved through the use of a convolutional neural network, and performance is reported as classification accuracy. Experiments are conducted on satellite SAR imagery. Specifically, we investigate Sentinel-1 imagery containing both damaged and non-damaged roads.