高密度城市绿化系统垂直分布特征提取仿真
Simulation of Vertical Distribution Feature Extraction for High-density Urban Greening System
何忧 1李鹏2
作者信息
- 1. 武汉华夏理工学院材料与工艺仿真实训中心,湖北 武汉 430074
- 2. 桂林电子科技大学海洋工程学院,广西 北海 536000
- 折叠
摘要
针对城市垂直绿化统筹缺失、分布不合理等问题,提出高密度城市绿化系统垂直分布特征提取方法.搭建以无人机为核心的遥感图像采集平台,采集绿化系统图像;建立卷积神经网络架构,通过残差学习去除初始图像噪声;利用缨帽变换算法旋转图像结构轴增强图像,突出图像细节信息;根据光谱的相似性与不连续性,采用数据驱动算法确立同质标准,将图像划分为不同区域,确保同一目标在相同区域内;分别从光谱、形状和纹理三个方面提取绿化系统垂直分布特征.实验结果表明,目标地区中,商业区与住宅区的垂直分布特征较为相似,但住宅区的绿化面积较大,商业区的植物种类更为丰富,根据提取得到的特征可以为绿化系统的合理规划提供参考.
Abstract
Due to unreasonable distribution of urban vertical green space,this paper puts forward a method for ex-tracting vertical distribution features of high-density urban greening system.Firstly,an acquisition platform of remote sensing images based on UAV was used to collect greening system images.Secondly,a convolutional neural network architecture was constructed,and then the noise was removed from the initial image through residual learning.The tas-seled hat transformation algorithm was adopted to rotate the image axis,thus enhancing the image,and highlighting the details.According to the similarity and discontinuity of the spectrum,a data-driven algorithm was adopted to deter-mine a homogeneity standard.After that,the image was divided into different areas to ensure that the same target was in the same area.Finally,the vertical distribution features of the greening system were extracted from three aspects:spectrum,shape and texture.Experimental results prove that the vertical distribution feature of the commercial area is similar with that of the residential area,but the green area of the residential area is larger,and the plant species in commercial area are more abundant.In addition,the extracted features can provide reasonable plan.
关键词
高密度城市/绿化系统/立体绿化/垂直分布/特征提取/缨帽变换算法Key words
High density city/Greening system/3D greening/Vertical distribution/Feature extraction/Tasseled Cap Transform引用本文复制引用
基金项目
湖北省教育厅哲学社会科学研究专项任务项目(2022)(22Z106)
湖北省教育科学规划课题(2023GB125)
湖北省自然科学基金(20215467746)
武汉华夏理工学院"通专融合"教学改革试点课程项目(TZ202301)
出版年
2024
NETL
NSTL
万方数据