利用混合光谱分解与SVM估算不透水面覆盖率

对比了线性混合光谱分解模型(SMA)与支持向量机(SVM)在TM影像上估算不透水面覆盖率(ISP)的精度,通过SVM模型拟合TM像元光谱特征与样本ISP间的关联而获得对未知像元ISP的估算能力。对于天津市主城区的TM影像,选择学校区、工矿区和住宅区的高分辨率影像分类结果作为训练样本(7020个)和验证样本(1500个),SVM模型的ISP估算均方差(15.4%)优于SMA估算结果(19.4%);在增加缨帽变化“绿度分量”及混合光谱分解“高反射率分量”作为SVM特征变量后,ISP估算精度提高为12%。研究结果表明：SVM模型能够拟合各像元光谱组分间非线性关系且具有较好小样本泛化的性能,适用于地面样本较少的大区域ISP制图;增加与ISP相关性大的光谱特征向量作为SVM输入能提供更多的区域地物空间分布信息,能够调整无样本的地表类型的ISP估算值,提高区域ISP估算的整体精度。     

The use of single-date MODIS imagery for estimating large-scale urban impervious surface fraction with spectral mixture analysis and machine learning techniques

Urban impervious surface information is essential for urban and environmental applications at the regional/national scales. As a popular image processing technique, spectral mixture analysis (SMA) has rarely been applied to coarse-resolution imagery due to the difficulty of deriving endmember spectra using traditional endmember selection methods, particularly within heterogeneous urban environments. To address this problem, we derived endmember signatures through a least squares solution (LSS) technique with known abundances of sample pixels, and integrated these endmember signatures into SMA for mapping large-scale impervious surface fraction. In addition, with the same sample set, we carried out objective comparative analyses among SMA (i.e. fully constrained and unconstrained SMA) and machine learning (i.e. Cubist regression tree and Random Forests) techniques. Analysis of results suggests three major conclusions. First, with the extrapolated endmember spectra from stratified random training samples, the SMA approaches performed relatively well, as indicated by small MAE values. Second, Random Forests yields more reliable results than Cubist regression tree, and its accuracy is improved with increased sample sizes. Finally, comparative analyses suggest a tentative guide for selecting an optimal approach for large-scale fractional imperviousness estimation: unconstrained SMA might be a favorable option with a small number of samples, while Random Forests might be preferred if a large number of samples are available.     

A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis,United States

This study developed an analytical procedure based upon a spectral unmixing model for characterizing and quantifying urban landscape changes in Indianapolis, Indiana, the United States, and for examining the environmental impact of such changes on land surface temperatures (LST). Three dates of Landsat TM/ETM+ images, acquired in 1991, 1995, and 2000, respectively, were utilized to document the historical morphological changes in impervious surface and vegetation coverage and to analyze the relationship between these changes and those occurred in LST. Three fraction endmembers, i.e., impervious surface, green vegetation, and shade, were derived with an unconstrained least-squares solution. A hybrid classification procedure, which combined maximum-likelihood and decision-tree algorithms, was developed to classify the fraction images into land use and land cover classes. Correlation analyses were conducted to investigate the changing relationships of LST with impervious surface and vegetation coverage. Results indicate that multi-temporal fraction images were effective for quantifying the dynamics of urban morphology and for deriving a reliable measurement of environmental variables such as vegetation abundance and impervious surface coverage. Urbanization created an evolved inverse relationship between impervious and vegetation coverage, and brought about new LST patterns because of LST's correlations with both impervious and vegetation coverage. Further researches should be directed to refine spectral mixture modeling by stratification, and by the use of multiple endmembers and hyperspectral imagery.     

Comparative analysis on utilisation of linear spectral unmixing and band ratio methods for processing ASTER data to delineate bauxite over a part of Chotonagpur plateau,Jharkhand, India

We have attempted comparative analysis of the utility of linear spectral unmixing (LSU) method and band ratios for delineating bauxite from laterite within the lateritic bauxite provinces of Chotonagpur Plateau, Jharkhand of India. This was attempted based on processing of visible–near infrared (VNIR) and shortwave infrared (SWIR) spectral bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. In LSU method, spectral features of main constituent minerals of lateritic bauxite are used to decompose the pixel spectra to estimate the relative abundance of bauxite and laterite in each pixel to spatially delineate bauxite within laterite. We have also compared the bauxite map derived using LSU method with bauxite maps of two band ratios in terms of spatial disposition of bauxite. We also have attempted to relate the abundance values of pixels of LSU-based bauxite map with band ratio values of bauxite pixels of two selected bauxite indices.     

Detection of dryland degradation using Landsat spectral unmixing remote sensing with syndrome concept in Minqin County,China

This study was to detect dryland degradation coupling linear spectral unmixing model of Landsat images with syndrome concept in temperate dryland system, Minqin, China. The phenological contrast and complementation between green vegetation fraction in summer, sandland fraction and saline land fraction in spring, was firstly structured to quantify degradation characteristics by simple correlation analysis with ground data. The spatiotemporal patterns of the three degradation indicators were interpreted with the help “dust bowl” syndrome, qualitatively deciphered the degradation causal clusters, loops and important consequences in the study area. The results indicate water-using and distribution pattern was changed, agricultural intensity and productivity increased, salinization lessened in oasis, whereas sandification risk heightened. This approach developed in this study, has the potentially broad applicability, for dryland system monitoring and modelling.