基于高光谱数据和雷达融合的滑坡信息提取

为了改进微地形滑坡遥感影像分类技术，从而提高微地形滑坡遥感信息提取的精度，采用湖北宜昌部分地区的无人机航拍高光谱影像(HSI)和激光雷达(LiDAR)数据作为研究数据源，并对高光谱和LiDAR数据进行融合，最后采用结合注意力模块(CBAM)的卷积神经网络(CNN)方法，对融合后的数据进行滑坡信息提取。研究表明，利用高光谱和雷达数据的优势，可以更准确地提取滑坡信息。

Landslide Information Extraction by Fusion of Hyperspectral and Radar Data

The aim of this research is to enhance the extraction accuracy by improving the classification of micro-terrain landslide remote sensing information. The landslide information in local areas of Yichang was extracted by using the method of Convolutional Neural Networks (CNN) combined with Convolutional Block Attention Module (CBAM) based on the fusion of Unmanned Aerial Vehicle (UAV) hyperspectral image (HSI) and Light Detection and Ranging (LiDAR) data. Results demonstrated that landslide information can be extracted with more accuracy based on the advantages of hyperspectral and radar data.