Based on deep learning, a Desertification Grassland Classification (DGC) and three-dimensional Convolution Neural Network (3D-CNN) model is established.
The F-norm paradigm is used to reduce the data; the data volume was effectively reduced while ensuring the integrity of the spatial information. Through structure and parameter optimization, the accuracy of the model is further improved by 9.8%, with an overall recognition accuracy of the optimized model greater than 96.16%.
Accordingly, high-precision classification of desert grassland features is achieved, informing continued grassland remote sensing research.
Read more at: https://link.springer.com/article/10.1007/s10812-020-01001-6
