Landslide image segmentation model based on multi-layer feature infor-mation fusion
Landslide cause serious harm to human living environment.The method of manually identifying the landslides is time-consuming and the hidden area is not easy to detect.The use of remote sensing image to identify the landslides can accu-rately and quickly realize the landslide disaster warning and rescue.With the rapid development of deep learning,semantic seg-mentation has been widely used in the field of landslide remote sensing image recognition.Aiming at the problems such as error recognition and image edge information loss in the current landslide image segmentation model,this paper proposes a landslide segmentation model MLFIF-Net,which integrates multi-layer feature information fusion.The model uses MobileNetv3-Small as the main trunk network to improve the feature extraction ability of the model.At the same time,a cascade spatial pyramid pool module is constructed to enhance the texture features of landslide images and obtain multi-scale information.An efficient channel attention module is used to focus on image features,and a multi-layer feature information fusion structure is designed to enhance the edge information of images,so as to improve the segmentation effect of the model.The experimental results show that the accuracy of the proposed model on the landslide data set of Bijie city,Guizhou province is 96.77％,the average accuracy of the class is 95.61％,and the average interaction ratio is 87.69％.Compared with SegNet and other six segmenta-tion models,its segmentation accuracy is better,and it can accurately identify the target area and highlight the edge details of the landslide image.
semantic segmentationremote sensing imagelandslidepyramid poolingattention modulefeature information fusion
万方数据
维普
