迁移学习支持下的高分影像山地滑坡灾害解译模型

An Automatic Interpretation Model for Mountains Landslide Disaster of High-Resolution Remote Sensing Images Based on Transfer Learning

无人机低空遥感是近年来新兴的一种快速获取灾情信息的手段,如何利用无人机高分影像构建滑坡灾害解译模型是实现快速自动解译滑坡的关键。针对该问题,对比了多种影像特征提取方法,将迁移学习(TL)特征和支持向量机(SVM)引入到构建滑坡灾害自动解译模型中,提出了一种TL支持下的高分影像滑坡灾害解译模型。选取5·12汶川地震及4·20芦山地震系列无人机影像构建了滑坡灾害样本库并进行了实验,TL特征方法整体分类准确度ACC为95%,ROC达到0.98,识别准确率达到97%。结果表明,所提方法可用于高分影像滑坡自动解译,同时可用于大面积高分影像中快速山地滑坡灾害定位及检测。

In recent years, low-altitude UAV remote sensing is emerging as a means to rapid access to disaster information. How to quickly and accurately interpret the landslide after a landslide disaster can provide reliable data sources for subsequent monitor and forecast. Then how to use UAV high spatial resolution images to construct an interpretation model for mountain landslide disaster is the key to achieve fast and automatic interpretation of landslide. To solve this problem, multiple image feature extracting methods were compared firstly. Then, the transfer learning （TL） and support vector machine（SVM） were introduced to construct the automatic interpretation model, and an automatic interpretation model for mountain landslide disaster with high spatial resolution images based on transfer learning method was proposed. Finally, to construct landslide disaster sample database, UAV series images of 5.12 and 4-20 earthquake were selected, and the experiments based on the database were carried out. The comprehensive classification accuracy ACC of transfer learning feature method was 95%, ROC was 0.98, and the recognition accuracy was 97%. Experimental results showed that the proposed method could be used for landslide automation interpretation with high spatial resolution remote sensing images, and quick location and detection of landslide disaster with high spatial resolution images for large area.