【摘要】 植被覆盖度(fractional vegetation coverage, FVC)是草地退化评价的重要指标之一，实时、快速、准确地采集FVC是进行草地退化评价的基础。本文以无人机(unmanned aerial vehicle, UAV)高光谱遥感图像为数据源，提出了3D-ResNet18深度学习覆盖度提取方法，将此方法与回归模型法和ResNet18经典深度学习方法进行比较，并对提取精度进行验证。结果表明，提出的3D-ResNet18方法对荒漠草原FVC展现出较优的提取效果，总体估算精度达97.56%,相比较NDVI、SA-VI、G＿CR＿NDVI、G＿CR＿SAVI和ResNet18分别提高了8.32%、5.92%、2.20%、2.14%和1.87%,为荒漠草原FVC信息高精度和高效率的统计奠定基础。更多还原

【Abstract】 Fractional vegetation coverage(FVC) is one of the important indicators for grassland degradation evaluation, and real-time, fast and accurate FVC acquisition is the basis for grassland degradation evaluation.This paper proposes a 3D-ResNet18 deep learning coverage extraction method using unmanned aerial vehicle(UAV) hyperspectral remote sensing images as the data source, compares this method with the regression model method and the ResNet18 classical deep learning method, and validates the extraction accuracy.The results show that the proposed 3D-ResNet18 method shows a better extraction effect on desert grassland FVC,with an overall estimation accuracy of 97.56%,which is 8.32%,5.92%,2.20%,2.14% and 1.87% higher compared to NDVI,SAVI,G＿CR＿NDVI,G＿CR＿ SAVI and ResNet18, respectively.The foundation for high-precision and efficient statistics of desert grassland FVC information is laid.更多还原