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Urban landcover mapping using different Spectral Mixture Analysis methods

机译：使用不同光谱混合分析方法的城市土地覆盖图

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摘要

The complex spatial and spectral variability of urban structures present fundamental challenges to deriving accurate remote sensing information for urban areas. Spectral mixture analysis (SMA), based on a physical mixture model, has ability to extract sub-pixel information such as the abundances of each endmember presented in the pixel (image unity). In this paper, different spectral mixture methods have been applied in order to examine the performance of each model in dealing with spectral variability of urban surface. The comparison is focused on linear spectral mixture analysis (LSMA) which is using a fixed number of endmembers for the entire scene and multiple endmember spectral mixture analysis (MESMA) which allows the number and types of endmembers to vary from pixel to pixel to extract the abundances of urban surface components. These techniques have been applied to map the physical components of urban land cover for the city of Bucharest, Romania, using Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and IKONOS imagery for 1989 - 2007 period. 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：城市结构复杂的空间和频谱可变性为推导城市区域的准确遥感信息提出了基本挑战。基于物理混合模型的光谱混合分析（SMA）具有提取子像素信息的能力，例如像素中呈现的每个末端成员的丰度（图像单位）。在本文中，为了检验每种模型在处理城市表面光谱变化方面的性能，已采用了不同的光谱混合方法。比较的重点是线性光谱混合分析（LSMA），它对整个场景使用固定数量的端成员，而多个端成员光谱混合分析（MESMA）允许端成员的数量和类型因像素而异，以提取像素。大量的城市表面成分。这些技术已应用Landsat专题地图绘制器（TM），Enhanced Thematic Mapper（ETM +）和IKONOS 1989年至2007年期间的图像对罗马尼亚布加勒斯特市的城市土地覆盖的物理成分进行了制图。本文展示了中高分辨率和高分辨率的多光谱图像在绘制和监视物理城市环境演变方面的潜力。

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