3D-CNN based UAV hyperspectral imagery for grassland degradation indicator ground object classification research

Abstract

The identification and counting of grassland degradation indicator ground objects is an important component of grassland ecological monitoring. These steps are also an important basis for developing ecological restoration and management programs for degraded grasslands. Compared with a traditional human survey, the use of remote sensing images can not only achieve dynamic monitoring of a large area, but also improve the efficiency. Recently, most studies regarding ground object classification based on remote sensing images address the development and optimization of classification models for features in several widely used datasets. For the remote sensing of desertified grasslands, remote sensing images with high spatial resolutions are used for studies on small and sparse features in degraded grasslands. The spatial resolution of the above mentioned datasets yields difficulties when attempting to classify small and sparse indicator features for desertified grasslands because generalization becomes limited. Therefore, establishing a lightweight classification model suitable for degraded grassland features with high spatial resolution is important. In this study, a low altitude unmanned aerial vehicle (UAV) hyperspectral remote sensing platform was constructed to collect high spatial resolution remote sensing images of degraded grasslands. The GDIF-3D-CNN classification model was used to classify the pure pixels and all pixels datasets, whose accuracy and efficiency were further improved by optimizing the eight parameters of the model. This study explores the remote sensing ground object classification of thin small plants and a large number of mixed pixels, realizing high precision classification among desertification degradation indicating plant populations of a species, and provides key quantitative data for grassland degradation research.