Optimization of the landslide identification method based on a dual attention mechanism

Abstract

With the development of computer vision technology, studies on landslide identification have gradually been carried out by means of deep learning. By introducing the dual attention model, an optimization algorithm for landslide image recognition based on a convolutional neural network is proposed in this paper. Based on 2 200 landslide image datasets, this paper discusses the effects of 10 network structures and 4 attention models on landslide recognition results. The effectiveness of this method is verified by using a 4∶1 training set and test set for landslide recognition. The results show that the ResNet structure performs better than other network structures. For this example, the ResNet-101 structure has the highest recall rate, precision rate and F1-measure. Compared with a single neural network, the convolutional neural network with a dual attention model has a higher accuracy of landslide identification, and the segmentation result of the landslide boundary is closer to the real landslide boundary. Among them, the ResNet-101+DAN model is the optimal model. In contrast, a single neural network cannot overcome the influence of the image noise, and the result of the image segmentation is poor.

Keywords

• landslide identification
• convolutional neural network
• automatic identification
• attention module
• landslide data set
• optimization of method