Multi-Source Remote Sensing Based Accurate Landslide Detection Leveraging Spatial-Temporal-Spectral Feature Fusion

Accurate landslide detection is extremely important in emergency rescue. Aiming at the problems concerning current landslide remote sensing detection that the fusion and utilization of spatial, temporal, and spectral features are poor in target detection model and the accuracy of recognition is unsatisfied, in this paper, we propose an accurate landslide detection method based on multi-source remote sensing images, leveraging the fusion of spatial, temporal, and spectral features. In specific, we construct a new multi-bands remote sensing image dataset, exploiting the registration of spectral and scale spaces based on the remote sensing image before and after landslide. Moreover, we combine the features of temporal variation, spectrum and spatial shape, which are transformed into the spectral representational model. And then, the support vector machine (SVM) algorithm is used to identify the landslide objects based on this new image dataset and representational model. Furthermore, we use the typical shape features, such as the axial aspect ratio, area and invariant moment, which are extracted for the fundamental shape models of landslides, to further classify these landslide objects. Finally, we conduct extensive experiments to evaluate the performance of the propose method by comparing with baseline methods. The experimental results show that our method outperforms the baseline algorithms, while achieving up to 95% accuracy in landslide detection.