This invention relates to an automatic classification method, apparatus, equipment, and medium for
grassland remote sensing resources, belonging to the fields of
remote sensing image processing and
machine learning. The method employs a three-layer classification framework, combining ground sample plots,
remote sensing data, meteorological data, and DSM
elevation data to progressively distinguish between
grassland and non-
grassland, coarsely classify grassland types (grassland, desert, shrubland, meadow, and
marsh), and refine them into 18 grassland resource categories.
Gaussian low-pass filtering is used for
noise reduction, weighted
pooling generates multi-scale feature maps, gated
convolution fills in missing regions, and an attention mechanism enhances the spatial autocorrelation of geographical features.
Random forest and convolutional neural networks are compared and optimized, and the optimal model is fused. Accuracy is evaluated based on UAV data and grassland resource maps, calculating the overall
macro-average accuracy, user accuracy, producer accuracy, F1 index, and Kappa coefficient. This invention achieves high-precision grassland resource classification and is suitable for
ecological monitoring and management of grassland resources.