Research on deep learning method recognition and a classification model of grassland grass species based on unmanned aerial vehicle hyperspectral remote sensing

Abstract

Identifying grass species in grasslands based on unmanned aerial vehicle hyperspectral remote sensing is the basis and premise of hyperspectral remote sensing when applied to grassland degradation monitoring and research. The small targets and mixed pixels involved grass species identification in grasslands creates problems, making identification cumbersome and classification accuracy difficult. This study involved the construction of an unmanned aerial vehicle hyperspectral remote sensing system using hyperspectral data of grass species in desert habitats that had been collected under natural light. A multi-resolution combined with a 1 × 1 feature map was formed by multiscale convolution, and grass species data were extracted from hyperspectral fine-grained feature data from grasslands. A recognition and classification model for degradation indicator species CNN was constructed using max pooling to retain the maximum amount of feature detail and up-sampling, reconstructing the feature space and feature fusion to smooth the edge texture of the data and enhance the weak data to alleviate the imbalance among samples. The results showed that the overall identification accuracy of the model for grassland species reached 98.78%, and the kappa coefficient reached 0.92, realizing the high-precision identification of grassland species, which laid the foundation for grassland species detection and research based on unmanned aerial vehicle hyperspectral imagery. In addition, the proposed degradation indicator species CNN model provides a useful reference for the identification and classification of small targets with mixed pixels.