1. 南京航空航天大学电子信息工程学院
2. 中国航空无线电电子研究所
纸质出版:2023
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张彩煜, 李明磊, 魏大洲, 等. 基于三维纹理的多模态特征城市土地利用分类[J]. 地理空间信息, 2023,21(9):14-17+26.
张彩煜, 李明磊, 魏大洲, et al. 基于三维纹理的多模态特征城市土地利用分类[J]. 2023, 21(9): 14-17+26.
城市土地利用分类研究对实现城市土地资源高效管理和城市可持续发展具有重要意义。基于无人机获取的遥感影像数据,提出了一种融合DSM特征、三维纹理特征和改进型植被指数的多模态特征提取技术,并利用支持向量机(SVM)分类器进行城市土地利用分类。针对传统绿蓝植被指数对无人机影像敏感度低的问题,提出了一种改进型绿蓝植被指数(MGBVI)。结果表明,采用多模态特征后分类精度提高了16.5%;多模态特征中选择三维纹理特征和MGBVI进行分类的效果最佳。
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Zhang Xianlong,Zhang Fei,Qi Yaxiao,Deng Laifei,Wang Xiaolong,Yang Shengtian.New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV)[J].International Journal of Applied Earth Observation and Geoinformation,2019.
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Bendig Juliane,Yu Kang,Aasen Helge,Bolten Andreas,Bennertz Simon,Broscheit Janis,Gnyp Martin L.,Bareth Georg.Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley.[J].International Journal of Applied Earth Observations and Geoinformation,2015.
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