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首页> 外文期刊>Journal of optoelectronics and advanced materials >Urban landcover mapping using Multiple Endmember Spectral Mixture Analysis
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Urban landcover mapping using Multiple Endmember Spectral Mixture Analysis

机译：使用多端元谱混合分析的城市土地覆盖图

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摘要

The spatial and spectral variability of urban environments are fundamental challenges in deriving accurate remote sensing information for urban areas. Multiple Endmember Spectral Mixture Analysis (MESMA) technique was used to map the physical components of urban land cover for the city of Constantza, Romania, using Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and IKONOS imagery during period of 1989 and 2006 years. Field spectra of vegetation, soil, and impervious surface areas collected with the use of a fine resolution and IKONOS image and pixel purity index tool in ENVI 4.3 software were modeled as reference endmembers in addition to photometric shade that was incorporated in every model. This study employs thirty endmembers and six hundred and sixty spectral models to identify soil, impervious, vegetation, and shade in the Constantza area. The mean RMS error for the selected land use land cover classes range from 0.0025 to 0.019. This paper demonstrates the potential of moderate-and high resolution, multispectral imagery to map and monitor the evolution of the physical urban environment.
机译：城市环境的空间和光谱可变性是在为城市地区获取准确的遥感信息时的基本挑战。使用多端元光谱混合分析（MESMA）技术，通过Landsat专题测绘仪（TM），增强型专题测绘仪（ETM +）和IKONOS影像，对罗马尼亚康斯坦察市的城市土地覆盖物的物理成分进行了制图，时间间隔为1989年， 2006年。除了在每个模型中都包含的光度阴影之外，还使用ENVI 4.3软件中使用高分辨率和IKONOS图像和像素纯度指数工具收集的植被，土壤和不渗透表面的场光谱建模为参考端元。这项研究采用了30个最终成员和660个光谱模型来识别康斯坦察地区的土壤，不透水，植被和阴影。所选土地利用土地覆盖类别的平均RMS误差范围为0.0025至0.019。本文展示了中高分辨率和高分辨率的多光谱图像在地图和监视物理城市环境的演变方面的潜力。

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