Accurate landslide detection leveraging UAV-based aerial remote sensing

Remote sensing by unmanned aerial vehicles (UAVs) is significantly important in emergency rescue applications and operations. Particularly, the on-site images from UAVs can provide valuable information for hazard identification and disaster assessment. In this study, the authors propose a novel method by using back propagation neural networks with feature fusion to detect landslides from UAV images. Specifically, the authors first construct a fundamental shape model of landslides and devise a scale-invariant feature transform algorithm for feature matching and transformation. By fusing the spatial shape features and spectral features of the landslide, the suspected landslide object from UAV images can be detected initially. Next, the change features of a pre/post-landslide object are extracted by using the satellite sensing images (before landslide) and the UAV image (after landslide). The authors further feed the change features into the proposed model to enhance the precision and accuracy of landslide detection. They conduct numerous experimental studies with aerospace remote sensing data in two real-world landslide scenarios. The evaluation results show that the proposed method outperforms baseline algorithms by achieving over 91% accuracy in landslide detection.