Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison
Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison
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DOI：
10.1080/01431160701469024
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发表时间：
2008-06
影响因子：
3.4
通讯作者：
Qihao Weng;Xuefei Hu;D. Lu
中科院分区：
文献类型：
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作者：
Qihao Weng;Xuefei Hu;D. Lu
对不透水表面的遥感估计对于监测城市发展和确定流域的总体环境健康具有重要意义，因此最近引起了越来越多的兴趣。本研究的主要目的是开发一种利用中空间分辨率卫星图像估算和绘制不透水地表的通用方法。我们将光谱混合分析（SMA）应用于美国印第安纳州马里恩县的地球观测1号（EO‐1）先进陆地成像仪（ALI）（多光谱）和Hyperion（高光谱）图像，计算植被、土壤、高反照率和低反照率的分数图像。根据三个标准对两种图像的有效性进行比较：(1)高质量的城市景观图像，(2)相对低的误差，(3)研究区域典型土地利用和土地覆盖（LULC）类型之间的区别。这些分数图像进一步用于估计和绘制不透水表面。利用数字正射影像四分之一四边形（DOQQ）图像对估计的不透水面的精度进行了检验。结果表明，ALI和Hyperion传感器在提取含SMA的分数图像和计算不透水表面方面都是有效的。使用四个端元的ALI和Hyperion图像的SMA结果都很好，两种情况下的平均均方根误差（RMSE）都小于0.04。ALI提取的不透水面图像的RMSE为15.3%，Hyperion提取的不透水面图像的RMSE为17.5%。然而，Hyperion图像在识别低反照率表面物质方面更强大，这一直是使用中分辨率多光谱图像估计不透水面的主要障碍。利用Hyperion波段组合的不同场景对不透水地表进行制图的敏感性分析表明，总体而言，制图精度的提高和对低反照率地表的识别能力的提高主要来自中红外区域的附加波段。
Remote sensing estimation of impervious surfaces is significant in monitoring urban development and determining the overall environmental health of a watershed, and has therefore recently attracted increasing interest. The main objective of this study was to develop a general approach to estimating and mapping impervious surfaces by using medium spatial resolution satellite imagery. We have applied spectral mixture analysis (SMA) to Earth Observing 1 (EO‐1) Advanced Land Imager (ALI) (multispectral) and Hyperion (hyperspectral) imagery in Marion County, Indiana, USA, to calculate the fraction images of vegetation, soil, high albedo and low albedo. The effectiveness of the two images was compared according to three criteria: (1) high‐quality fraction images for the urban landscape, (2) relatively low error, and (3) the distinction among typical land use and land cover (LULC) types in the study area. The fraction images were further used to estimate and map impervious surfaces. The accuracy of the estimated impervious surface was checked against Digital Orthophoto Quarter Quadrangle (DOQQ) images. The results indicate that both ALI and Hyperion sensors were effective in deriving the fraction images with SMA and in computing impervious surfaces. The SMA results for both ALI and Hyperion images using four endmembers were excellent, with a mean root mean square error (RMSE) less than 0.04 in both cases. The ALI‐derived impervious surface image yielded an RMSE of 15.3%, and the Hyperion‐derived impervious surface image yielded an RMSE of 17.5%. However, the Hyperion image was more powerful in discerning low‐albedo surface materials, which has been a major obstacle for impervious surface estimation with medium resolution multispectral images. A sensitivity analysis of the mapping of impervious surfaces using different scenarios of Hyperion band combinations suggests that the improvement in mapping accuracy in general and the better ability in discriminating low‐albedo surfaces came mainly from additional bands in the mid‐infrared region.