高分辨率影像城市植被自动提取算法

近十几年来,随着城市化进程加剧,准确获取城市植被的分布信息,是城市气候和地表能量平衡研究的重要内容。高空间分辨率遥感影像数据,为精确获取和动态监测城市植被提供了重要资料。本研究利用资源三号数据对长江三角洲地区城市植被进行光谱特征分析与提取,提出一种城市植被的自动化信息提取算法—分离面法（Hyperplanes for Plant Extraction Methodology,HPEM）。结果表明：在假彩色反射率空间,植被与NDVI值低的背景有很好的分离性,而在真彩色反射率空间,植被与NDVI值高的背景有很好的分离性;HPEM能很好地避免NDVI最佳阈值法中将建筑物误分为植被的问题,其精度明显优于NDVI最佳阈值法,Kappa系数从0.85提高到0.90,总的错分与漏分误差从21.15%降低到14.18%。可见,本文的HPEM方法能有效提高城市植被信息自动提取的精度。

Automatic Urban Vegetation Extraction Method Using High Resolution Imagery

With the progress of sustained urbanization in the past ten years, information of accurate urban vegetation cover is turning to be essential for the study of both regional climate and urban energy balance. High spatial-resolution remote sensing imagery provides an important tool for automatic mapping and monitoring of urban vegetation cover due to its broad coverage and high-spatial resolution. We propose an automatic urban vegetation extraction methodology, named as hyperplanes for plant extraction methodology (HPEM), based on the vegetation spectral feature analysis with the ZY-3 multi-spectral imagery over different cities in Yangtze River Delta. The results showed that: first, the vegetation pixels and non-vegetation pixels with low NDVI value can be well separated in the false color composite reflectance space, while the vegetation pixels and non-vegetation pixels with high NDVI value can be well separated in the true color composite reflectance space; second, HPEM could effectively suppress the errors of commission that come from built-up pixels which was often misclassified in NDVI method. HPEM’s performance was better than NDVI at the optimal threshold, with kappa coefficients increased from 0.85 to 0.90 and the total errors of omission and commission reduced from 21.15% to 14.18%. Compared to NDVI method, HPEM also avoided the tedious trial-and-error procedures for searching the optimal threshold. Therefore, HPEM can effectively improve the accuracy of automatic urban vegetation mapping. Moreover, the urban vegetation products are more reliable for further urban environment research.