A study of high-resolution remote sensing image landslide detection with optimized anchor boxes and edge enhancement

Abstract

ABSTRACTThis paper takes landslide as a special research object. For the problems of landslide detection in remote sensing images, deep learning and playback method is adopted. Using the You Only Look Once v5 network (YOLOv5) in combination with the Gabor filter, its edge detection, detection anchor frame and small object detection scale are improved and optimized. The YOLOv5(ISODATA) model was finally established for landslide image detection by incorporating the edge control factor and four clustering algorithms (K-means, K-means + +, k-medoid, and Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to evaluate the accuracy of the detection anchor frame and add small target large-scale sampling. Three target identification models – YOLOv5, Region Convolution Neural Network (R-CNN), and Fast R-CNN – are experimentally compared in order to assess the effectiveness of the proposed method. According to the results of experiments, the proposed method outperforms the other three detection models with an AUC of 0.921, a recall of 86.14%, and an MCC of 0.887. It further demonstrates the method’s positive impact on landslide remote sensing image recognition and its ability to solve related issues.