Based on deep learning, a desertification grassland classification (DGC) and three-dimensional convolution neural network (3D-CNN) model is established. The F-norm² 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.

中文翻译：

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沙漠化玻璃地分类和三维卷积神经网络模型的无人机高光谱遥感影像识别沙漠草原地貌

在深度学习的基础上，建立了荒漠化草原分类（DGC）和三维卷积神经网络（3D-CNN）模型。F-范数²范式用于减少数据。在确保空间信息完整性的同时，有效减少了数据量。通过结构和参数优化，模型的准确度进一步提高了9.8％，优化后的模型的整体识别准确度大于96.16％。因此，实现了沙漠草原特征的高精度分类，为继续进行草地遥感研究提供了依据。