Desertification Glassland Classification and Three-Dimensional Convolution Neural Network Model for Identifying Desert Grassland Landforms with Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images
Desertification Glassland Classification and Three-Dimensional Convolution Neural Network Model for Identifying Desert Grassland Landforms with Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images
复制标题
无人机高光谱遥感影像荒漠化草原分类及荒漠草原地貌识别三维卷积神经网络模型
DOI：
10.1007/s10812-020-01001-6
复制
发表时间：
2020-05-21
影响因子：
0.7
通讯作者：
Zhu, X.
中科院分区：
文献类型：
--
作者：
Pi, W.;Du, J.;Zhu, X.
基于深度学习，建立了荒漠化草地分类（DGC）和三维卷积神经网络（3D-CNN）模型。采用F-norm 2范式对数据进行约简，在保证空间信息完整性的同时有效地减少了数据量。通过结构和参数优化，模型的准确率进一步提高了9.8%，优化后的模型总体识别准确率大于96.16%。从而实现了荒漠草地特征的高精度分类，为草地遥感研究提供了依据。
Based on deep learning, a desertification grassland classification (DGC) and three-dimensional convolution neural network (3D-CNN) model is established. The F-norm2paradigm 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.