Deep Learning Classification of Grassland Desertification in China via Low-Altitude UAV Hyperspectral Remote Sensing

Abstract

Efficiency and accuracy are major bottlenecks in conducting ecological surveys and acquiring statistical data concerning grassland desertification. Traditional manual ground-based surveys are inefficient, and aerospace-based remote sensing surveys are limited by low spatial resolution and accuracy. In this study, we propose a low-altitude unmanned aerial vehicle (UAV) hyperspectral visible near-infrared (vis-NIR) remote sensing hardware platform, which combines efficiency and accuracy for high-precision remote sensing-based ecological surveys and statistical data collection on grassland desertification. We use the classical deep learning network models VGG and ResNet and their corresponding improved 3D convolutional kernels: 3D-VGG and 3D-ResNet, respectively, to classify the collected data into features. The results show that the two classical models yield good results for vegetation and bare soil in desertified grasslands, and the 3D models yield superior classification results for small sample features. Our results can serve as benchmarks for hardware integration and data analysis for remote sensing-based grassland desertification research and lay the foundation for further finer classifications and more accurate statistics of features.