【作者】 吕杰；

【导师】 刘湘南；

【作者基本信息】 中国地质大学（北京） ， 地图制图学与地理信息工程， 2009， 硕士

【摘要】 快速获取城市绿化信息对开展城市绿地保护、规划与建设城市绿地,对提高人们的环保意识、完善城市地价体系、规范绿地的经营管理等,都具有重要的理论和实践意义。传统的地面调查和测量方法费时、费力,并且动态更新比较困难,遥感技术提供了一种快速、宏观、动态提取城市绿地信息的方法。对城市信息提取来说,一些基于遥感图像分类和植被指数的传统信息提取方法通常是不准确的。论文首先对Hyperion数据进行预处理。通过掩膜处理,得到研究区范围内的Hyperion影像。通过波段选取,剔除了未定标的波段和水汽影响严重的20个波段以及SWIR与VNIR重复的波段。通过进行辐射定标,将Hyperion数据由DN值转化成辐射亮度值。通过坏线修复和条带去除得到更为准确的图像。通过大气辐射校正获取Hyperion数据反应的地表真实物理模型参数。本文采用的是基于MODTRAN4的FLAASH(Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes)大气纠正模块,大气辐射校正之后得到高光谱遥感影像中复原地物的地表反射率。应用线性混合光谱模型提取绿地信息。首先运用PPI (纯净像元指数)进行样本提纯,得到最纯净的像元。同时,利用N-D散度法进行样本重组,确定最终光谱端元。然后利用线性光谱模型分解混合像元的方法对研究区高光谱遥感数据提取绿地信息,并与ISODATA法、监督分类中的最大似然分类方法(MLC)和NDVI密度分割方法的提取城市绿地结果进行比较。结果表明,对城市绿地绿地信息而言,混合像元分解的总体精度和Kappa系数仅低于最大似然分类法,高于其他绿地提取方法。而混合像元分解方法制图精度远远高于最大似然分类方法,同时,混合像元分解方法的RMSE为0.00237,显示了较高的信息提取精度。因此,应用混合像元分解方法提取城市绿地更具有优势。未来的研究将引入地面成像光谱仪来提供更加精确的地物纯净端元信息,进一步的研究将结合多个时相的Hyperion数据来研究城市绿地和城市其他地物组分信息的动态变化情况。更多还原

【Abstract】 To obtain the urban compositional information quickly is of great theoretical and practical significance , it can increase the public environment protection consciousness and perfect the urban land price system, furthermore, it is of great importance for the regulation of green space management. Remote sensing technology offers an alternative to traditional ground-based survey of these green spaces. Traditional methods for the mapping of green space from remote sensing data such as classification techniques and vegetation indices were found to be inaccurate.In this study we apply a linear spectral unmixing approach to hyperspectral data to map urban green space. Spectral mixing analysis(SMA) is an model based on the linear mixing of two or more pure spectral endmembers , it allows for variability in composition and illumination within an image. SMA gives the opportunity to categorise the scene into various sub-areas and guilds the endmember selection for a better adjustment of the model to different surface types.EO-1 Hyperion data was used for this study, the Hyperion gathers near-continuous data in 242 discrete narrow bands along the 400–2500 nm spectral range at a 30 m spatial resolution and in 16 bits, and allows the relative contributions of different materials to the spectrally heterogeneous radiance field to be determined and their abundance to be mapped. Data processing occurred in several steps. The first of these was to select the useful bands, of the original 242 Hyperion bands, 176 bands were unique and calibrated, other bands were therefore dropped.Then, the original Digital Number (DN) of Hyperion from the 176 bands were converted to radiance , using the DN-to-radiance conversion factors that accompanied the Hyperion data. The next step of preprocessing was the atmospheric correction to convert the radiance to surface reflectance. We processed the data using the above water reflectance spectra with an atmospherically corrected image. Images were corrected using the ENVI FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction software package. The FLAASH module incorporates MODTRAN 4 radiation transfer code with all MODTRAN atmosphere and aerosol types to calculate a unique solution for each image.Finally, we specifically used a linear SMA (LSMA) model and performed a minimum noise fraction (MNF) transformation and pixel purity index (PPI) on EO-1 Hyperion image to derive the proportion of ground cover (green space,buildsings ) and water within a pixel.With the endmember spectra, a fully constrained linear mixture analysis was performed to generate fraction images for each endmember. The resulting green space fraction map showed the distribution and relative abundance of its endmember in the field.The overall root mean square (RMS) errors was 0.237%, unmixing errors occurred mainly due to multiple scattering as well as close endmember spectral correlation. The result indicates that spectral unmixing applied to hyperspectral imagery can be a useful tool for mapping green space in the urban area. Compared with other information extraction methods, we found that LSMM got better green space information results than ISODATA method、Maximum Likelihood Classification (MLC) method and NDVI density slice method, thus indicate that LSMM is a better information extraction method for extracting green space in the research area.Future work will incorporate the imaging spectrometry to provide more accurate endmembers of the urban area as a reference, additionally, to detect the green space or other physical compositions, a time series of Hyperion data will be analyzed with the application of LSMM.更多还原