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.
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Published in Zhurnal Prikladnoi Spektroskopii, Vol. 87, No. 2, pp. 296–305, March–April, 2020.
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Pi, W., Du, J., Liu, H. et al. Desertification Glassland Classification and Three-Dimensional Convolution Neural Network Model for Identifying Desert Grassland Landforms with Unmanned Aerial Vehicle Hyperspectral Remote Sensing Images. J Appl Spectrosc 87, 309–318 (2020). https://doi.org/10.1007/s10812-020-01001-6
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DOI: https://doi.org/10.1007/s10812-020-01001-6