Study on Combining Two Faster R-CNN Models for Landslide Detection with a Classification Decision Tree to Improve the Detection Performance

Abstract

This study aims to improve the accuracy of landslide detection in satellite images by combining two object detection models based on a faster region-based convolutional neural network (Faster R-CNN) with a classification decision tree. The proposed method combines the predicted results from the two Faster R-CNN models and classifies their features with a classification decision tree to generate a bounding-box that surrounds the landslide area in the input image. The first Faster R-CNN model is trained by using a training set of color images (RGB images). The second model is trained by using grayscale images that represent digital elevation models (DEMs). The results from both models are used to construct features for training a classification decision tree. The resulting bounding-box is selected from the following four classes: the box obtained from the RGB model, the box obtained from the DEM model, the intersection of those two boxes, and the smallest box that contains the union of them. The evaluation results show that the proposed method is better than the RGB model in terms of accuracy, precision, recall, F-measure, and Intersection-over-Union (IoU) score. It is slightly better than the DEM model in almost all evaluation metrics, except the precision.

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