基于三维深度学习模型的沙漠草原高光谱图像分类

IF 0.7 Q4 AGRICULTURAL ENGINEERING
Ronghua Wang, Yanbin Zhang, J. Du, Yuge Bi
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引用次数: 1

摘要

植被的识别和分类是草原退化监测、分类和量化研究的基础。在这里,使用四个深度学习模型对沙漠草原的无人机高光谱遥感图像进行分类。VGG16和ResNet18对植被和裸土实现了更好的图像分类结果,而通过3D卷积核改进的三维(3D)-VG16和3D-ResNet18对图像中的植被、裸土和小样本特征实现了更好地分类。对每个模型的卷积核数量、卷积核大小和批量大小参数进行了优化,3D-ResNet18-J具有最佳的分类性能,整体分类准确率为97.74%。它在沙漠草原无人机高光谱遥感图像分类中实现了高精度和高效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HYPERSPECTRAL IMAGE CLASSIFICATION IN DESERT GRASSLAND BASED ON THREE-DIMENSIONAL DEEP LEARNING MODEL
Identification and classification of vegetation are the basis for grassland degradation monitoring, classification and quantification studies. Here, four deep learning models were used to classify the unmanned aerial vehicle (UAV) hyperspectral remote sensing images of desert grassland. VGG16 and ResNet18 achieved better image classification results for vegetation and bare soil, whereas three-dimensional (3D)-VGG16 and 3D-ResNet18, improved by 3D convolutional kernels, achieved better classification for vegetation, bare soil and small sample features in the images. The number of convolutional kernels, its size and batch size parameters of each model were optimised, and 3D-ResNet18-J had the best classification performance, with an overall classification accuracy of 97.74%. It achieved high precision and efficiency in classifying UAV hyperspectral remote sensing images of desert grassland.
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来源期刊
INMATEH-Agricultural Engineering
INMATEH-Agricultural Engineering AGRICULTURAL ENGINEERING-
CiteScore
1.30
自引率
57.10%
发文量
98
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