Abstract
Accurate spatial distribution of grassland degradation indicator species is of great significance for grassland degradation monitoring. In order to realize the intelligent remote sensing grassland degradation monitoring task, this paper collects remote sensing data of three degradation indicator species of desert grassland, namely, constructive species, dominant species, and companion species, through the UAV hyperspectral remote sensing platform, and proposes a multi-feature fusion (MFF) classification model. In addition, vertical convolution, horizontal convolution, and group convolution mechanisms are introduced to further reduce the number of model parameters and effectively improve the computational efficiency of the model. The results show that the overall accuracy and kappa coefficient of the model can reach 91.81% and 0.8473, respectively, and it also has better classification performance and computational efficiency compared to different deep learning classification models. This study provides a new method for high-precision and efficient fine classification study of degradation indicator species in grasslands.
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