USING GENERATIVE ADVERSARIAL NETWORKS FOR EXTRACTION OF INSAR SIGNALS FROM LARGE-SCALE SENTINEL-1 INTERFEROGRAMS BY IMPROVING TROPOSPHERIC NOISE CORRECTION

We found that the altitude (< 240 m) and distance to rivers (< 300 m) emerged as important factors for the cause of landslides. LR-MLP-Boosting achieves the highest prediction accuracy. The coupling models outperform the corresponding single models and Boosting algorithm performs better than the Bagging algorithm. High-quality non-landslide samples enhance the accuracy of LSM. They can be effectively obtained by using the LR model to constrain its selection range.