摘要
城市用地功能分类的准确识别对精准把握城市现状、优化城市空间结构有重要意义。基于此,利用高分辨力遥感影像,提出一种针对中国城市用地功能分类的模型。设计一种多分辨力特征融合的卷积神经网络识别遥感影像中的特定功能区;针对中国城市功能区分布的特点,建立一个用于城市用地功能分类的新数据集。实验显示,本文算法在6种用地功能类型上的分类精确度达88%,表明算法对城市用地功能分类识别具有较高的准确性。最后,通过对北京部分主要城区的案例研究,验证了所提出的模型在城市规划相关领域提供数据支持的价值和有效性。
Urban land use classification is of great significance for capturing current situation of cities accurately and optimizing urban spatial structure.An urban land use classification model specifically for China is proposed by using remote sensing images.Firstly,a multi-resolution feature fusion convolution neural network is designed to recognize urban land use types.Besides,according to the distribution characteristics of urban functional areas in China,a new dataset for the urban land use classification is proposed.Experimental results show that the proposed work can reach 88%accuracy on six urban land use types,which validates the effectiveness of the algorithm in the classification of urban land use.Finally,a case study for part of Beijing’s main urban districts demonstrates the value and effectiveness of the proposed model for providing data support in the field of urban planning.
作者
刘美
卿粼波
韩龙玫
许盛宇
LIU Mei;QING Linbo;HAN Longmei;XU Shengyu(School of Electronic Information Engineering,Sichuan University,Chengdu Sichuan 610065,China;Chengdu Institute of Planning&Design,Information Center,Chengdu Sichuan 610065,China)
出处
《太赫兹科学与电子信息学报》
2021年第1期132-137,共6页
Journal of Terahertz Science and Electronic Information Technology
基金
国家自然科学基金资助项目(61871278)
四川省科技计划资助项目(2018HH0143)
四川省教育厅科研资助项目(18ZB0355)。
关键词
遥感影像
用地功能分类
神经网络
城市规划
remote sensing images
land use classification
neural network
urban planning
