城市绿地是城市生态系统的重要组成部分,具有生态、景观、文化、健康等多重作用,准确获取城市绿地空间分布能够为城市可持续发展提供科学依据和支持.利用资源三号遥感影像及归一化植被指数提取城市绿地,存在水体、蓝顶建筑、建筑物阴影等城市背景地物对绿地提取造成干扰的问题.为此,提出一种复杂城市环境下的资源三号遥感影像绿地提取方法.该方法提出抗低亮度像元干扰的绿地提取特征,并在HSI(Hue-Saturation-Intensity)颜色空间中设计对象级阴影提取特征,进而在阴影内外部计算不同绿地提取特征并进行阈值分割,实现城市绿地遥感提取.以南京、武汉、乌鲁木齐、沈阳等10个城市为研究区进行精度评价,结果表明:(1)使用本文方法提取城市绿地的整体精确率、召回率、F1值和交并比依次为93.28％、92.60％、92.91％和86.76％;(2)抗低亮度像元干扰的绿地提取特征性能优于RVI、NDVI、DVI,与Deeplab V3+、Segformer、UPerNet等相关模型相比,提出方法整体上有较优异的性能表现,并且提取精度优于典型城市绿地遥感制图产品UGS-1 m(Urban Green Space-1 m);(3)从局部提取细节来看,提出方法能够有效区分绿地与水体及蓝顶建筑,并对建筑阴影区内部植被具备提取效果.本文提出方法对于利用资源三号遥感影像快速高效地开展城市绿地空间业务化遥感监测具有重要意义.
Green space extraction method from ZY-3 remote sensing images in complex urban environment
Urban green space is an important part of the urban ecosystem,providing multiple functions such as ecology,landscape,culture,and health benefits.Obtaining the spatial distribution of urban green space accurately can offer a scientific basis and support for sustainable urban development.However,using ZY-3 remote sensing images and the Normalized Difference Vegetation Index(NDVI)to extract urban green spaces,urban background features such as water,blue-roofed buildings,and shadows may be confused with urban green space.To address this issue,we proposed an extraction method of urban green space for ZY-3 remote sensing images in complex urban environments.This method introduced extraction features of green space that are robust to low-brightness pixels,and designed object-level extraction features of shadow in the Hue-Saturation-Intensity(HSI)color space.Then it applied different features to perform threshold segmentation inside and outside shadow areas,achieving the extraction of urban green space.The accuracy of the proposed method was evaluated using the 10 cities including Nanjing,Wuhan,Urumqi,and Shenyang,as research areas.The results showed that:(1)The overall precision,recall,F1 value,and IoU(Intersection of Union)of the proposed method were 93.28％and 92.60％,92.91 and 86.76％.(2)The proposed extraction feature of green space robust to low-brightness pixels outperformed RVI,NDVI,and DVI.Compared with related models such as Deeplab V3+,Segformer and UPerNet,the proposed method had better overall performance,and its extraction accuracy was superior to the product Urban Green Space-1 m(UGS-l m);(3)From the perspective of local extraction details,the proposed method can effectively distinguish green spaces from water and blue-roofed houses,and extract vegetation inside the shadow areas of buildings.The proposed method is of great significance for quickly and efficiently conducting operational remote sensing monitoring of urban green space.
remote sensingurban green spacehigh-resolution remote sensingZY-3Normalized Difference Vegetation Index(NDVI)shadow extraction
万方数据
维普
