星-地协同多源遥感数据不透水面辨识机理与反演模型研究

当前，针对不透水面信息提取的研究大多是基于中低光谱分辨率的卫星影像来开展的。由于各种不透水材料的光谱具有明显的异质性，采用中低光谱分辨率影像提取的不透水面信息往往会混有土壤、植被阴影等透水地物，提取的精度不高。为了解决这一问题，本项目主要从光谱分辨率入手，开展了城市不透水面信息的提取技术研究。采用多光谱影像数据（Worldview系列、Landsat系列、SPOT 系列、EO-1 ALI等影像）、高光谱影像数据(EO-1 Hyperion影像)和地面光谱实测数据等“星-地协同”多源遥感数据，研究城市不透水面信息的辨识机理和反演模型，进一步利用高光谱模型来辅助多光谱影像数据进行不透水面的提取验证，从而对现有的不透水面反演模型进行改进，力求解决不透水面信息与土壤、植被阴影等地物相互混淆的问题。

Currently, studies on impervious surface retrieval are mainly based on moderate-spectral resolution satellite imagery. As impermeable materials have high heterogeneity in spectrum, the extracted impervious surface information from the moderate spectral resolution satellite images is often mixed with non-impervious surface such as soil and vegetation shadow, which causes the lowering of the accuracy of impervious surface extraction. In order to address the problem, this project carries out the study on information extraction of urban impervious surface by taking spectral resolution as the key point. The multi-spectral image data (such as Worldview series, Landsat series, SPOT series and EO-1 ALI image), hyperspectral image data (such as EO-1 Hyperion image) and ground-measured hyperspectral data which are called "satellite-ground synergy" multi-source remote sensing data are used as data sources. The recognition mechanism and retrieval models of impervious surface are performed. The hyperspectral models of impervious surface are contributed to verifying extraction model of impervious surface from multi-spectral images, and thus to improve the existing impervious surface retrieval models. Finally, the problem of mixing impervious surface with soil and vegetation shadow is solved.