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1.
The spatial and spectral variability of urban environments present fundamental challenges to deriving accurate remote sensing products for urban areas. Multiple endmember spectral mixture analysis (MESMA) is a technique that potentially addresses both challenges. MESMA models spectra as the linear sum of spectrally pure endmembers that vary on a per-pixel basis. Spatial variability is addressed by mapping sub-pixel components of land cover as a combination of endmembers. Spectral variability is addressed by allowing the number and type of endmembers to vary from pixel to pixel. This paper presents an application of MESMA to map the physical components of urban land cover for the city of Manaus, Brazil, using Landsat Enhanced Thematic Mapper (ETM+) imagery.We present a methodology to build a regionally specific spectral library of urban materials based on generalized categories of urban land-cover components: vegetation, impervious surfaces, soil, and water. Using this library, we applied MESMA to generate a total of 1137 two-, three-, and four-endmember models for each pixel; the model with the lowest root-mean-squared (RMS) error and lowest complexity was selected on a per-pixel basis. Almost 97% of the pixels within the image were modeled within the 2.5% RMS error constraint. The modeled fractions were used to generate continuous maps of the per-pixel abundance of each generalized land-cover component. We provide an example to demonstrate that land-cover components have the potential to characterize trajectories of physical landscape change as urban neighborhoods develop through time. Accuracy of land-cover fractions was assessed using high-resolution, geocoded images mosaicked from digital aerial videography. Modeled vegetation and impervious fractions corresponded well with the reference fractions. Modeled soil fractions did not correspond as closely with the reference fractions, in part due to limitations of the reference data. This work demonstrates the potential of moderate-resolution, multispectral imagery to map and monitor the evolution of the physical urban environment.  相似文献   

2.
中国正在经历快速地城市化过程,及时又准确地掌握城市化过程对我国社会经济发展具有重要的实际意义。以Landsat-TM和ETM+为主要数据源,通过多端元光谱混合分析法(MESMA)提取北京建成区不透水层的时空演变信息。在Ridd的V-I-S(植被—不透水层—土壤)概念模型框架下,基于最小噪音变换(MNF)将TM或ETM+的6个光谱波段转换成MNF空间,并定义4种端元光谱分别代表植被、高反射率地表、低反射率地表和土壤,同时构建北京建成区端元光谱数据库。然后在MATLAB软件包中实现MESMA模型程序,依次提取北京市6个时段的不透水层信息。研究结果表明:MESMA方法能够提高植被、土壤和不透水层提取精度,相对误差分别为14.6%、17.3%和11.9%。研究结论充分说明MESMA方法应用到一个时间序列的中分辨率多光谱遥感影像是非常有效的。MESMA光谱分解方法能高效实现北京城市动态变化和城市扩张的监测。  相似文献   

3.
Spectral mixture analysis is probably the most commonly used approach among sub‐pixel analysis techniques. This method models pixel spectra as a linear combination of spectral signatures from two or more ground components. However, spectral mixture analysis does not account for the absence of one of the surface features or spectral variation within pure materials since it utilizes an invariable set of surface features. Multiple endmember spectral mixture analysis (MESMA), which addresses these issues by allowing endmembers to vary on a per pixel basis, was employed in this study to model Landsat ETM+ reflectance in the Phoenix metropolitan area. Image endmember spectra of vegetation, soils, and impervious surfaces were collected with the use of a fine resolution Quickbird image and the pixel purity index. This study employed 204 (3×17×4) total four‐endmember models for the urban subset and 96 (6×6×2×4) total five‐endmember models for the non‐urban subset to identify fractions of soil, impervious surface, vegetation and shade. The Pearson correlation between the fraction outputs from MESMA and reference data from Quickbird 60 cm resolution data for soil, impervious, and vegetation were 0.8030, 0.8632, and 0.8496 respectively. Results from this study suggest that the MESMA approach is effective in mapping urban land covers in desert cities at sub‐pixel level.  相似文献   

4.
混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。  相似文献   

5.
基于线性混合模型的高光谱图像端元提取   总被引:16,自引:0,他引:16  
近年来,基于线性混合模型的光谱解混合技术正在越来越广泛地用在光谱数据分析和遥感地物量化中,这项技术的关键就在于确定端元(Endmember)光谱。通常,端元的荻取有两种方式:来源于光谱库以及来源于图像数据,相比之下后者得到的结果更能体现真实的地面信息。为此,从线性混合模型的特点出发,归纳了目前几种比较成熟的端元提取算法,分析了它们的主要思想和存在的优缺点,并总结了评估算法结果的依据,最后介绍了端元提取技术的发展趋势。  相似文献   

6.
In urban areas, spectral mixture analysis (SMA) is a common technique for deriving the fractions of land covers within a pixel and information on the distribution of impervious surfaces. This study examined how the selection of endmembers affected the quantification of impervious surfaces using TM and ASTER imagery. Multiple subsets of endmembers derived using (1) extreme pixels from a minimum noise fraction (MNF) transformation, and (2) a manual approach using a priori knowledge of the study area were analysed. Two data sets were used to assess accuracy: (1) simulated image data comprising unmixed and mixed pixels of 10 typical and spectrally different urban land covers, and (2) detailed data derived from high-resolution aerial photography. The dimensionality of the imagery limited the number of endmembers, and as a result, unmixed land covers were modelled using multiple endmembers and some cells had abundance values that summed to more than one or were negative. The land covers of red roofs and concrete were the largest contributors to the error in impervious surfaces. The Sequential Maximum Angle Convex Cone (SMACC) endmember model was also used to unmix the images; however, the larger number of endmembers did not resolve the use of multiple endmembers to model the unmixed land covers and the accuracy was similar to that using SMA. The relationship between the pervious fraction estimated using the vegetation endmember and the ground reference data was stronger than that for the impervious fraction, although the fraction was underestimated. The problems in modelling highly variable impervious surfaces with a limited number of endmembers suggest that in urban environments with substantial vegetation, modelling the vegetation component as the inverse of the impervious fraction may lead to improved results.  相似文献   

7.
Successful discrimination of a variety of natural and urban landscape components has been achieved with remote sensing data using multiple endmember spectral mixture analysis (MESMA). MESMA is a spectral matching algorithm that addresses spectral variability by allowing multiple reference spectra (i.e., endmembers) to represent each material class. However, materials that have a high-degree of spectral similarity between classes, such as similar plant-types or closely related plant species, and large variations in albedo present an ongoing challenge for accurate class discrimination with imaging spectrometry. Continuum removal (CR) analysis may improve class separability by emphasizing individual absorption features across a normalized spectrum. The spectral and structural characteristics common to most Eucalyptus trees make them notoriously difficult to discriminate in closed-canopy forests with imaging spectrometry. We evaluated whether CR applied to hyperspectral remote sensing data improved the performance of MESMA in classifying and mapping nine eucalypt tree species according to the two major Eucalyptus subgenera, Eucalyptus (common name “monocalypt”) and Symphyomyrtus (common name “symphyomyrtle”). Mixed-canopies comprised of monocalypts and symphyomyrtles are common in Australia, although their spatial distribution is not random. The ability to map these functional types on a landscape-scale could provide important information about ecosystem processes, landscape disturbance history and wildlife habitat. We created a spectral library of 229 pixels from 37 symphyomyrtle tree canopies and 406 pixels from 62 monocalypt tree canopies selected from HyMap imagery and verified with field data. Based on these reference data, we achieved overall classification accuracies at the subgenera-level of 75% (Kappa 0.48) for non-CR spectra and 83% (Kappa 0.63) for the CR spectra. We found that continuum-removal improved the classification performance of most endmember-models, although a larger portion of pixels remained unmodeled with the CR spectra (2%) compared to the non-CR spectra (0%). We utilized a new method for model optimization and created maps of monocalypt and symphyomyrtle distribution in our study area based on our best performing endmember-models. Our vegetation maps were largely consistent with our expectations of subgenera distribution based on our knowledge of the region.  相似文献   

8.
光谱可变性是影响高光谱图像光谱混合分析精度的重要因素,多端元光谱混合分析是解决该问题的有效手段。为了降低光谱混合分析时间复杂度的同时提高其精度,提出了一种由粗到细的多端元光谱混合分析算法,该算法首先基于扩展的端元集对每个像元进行全约束光谱混合粗分析,确定含所有地物的初始端元集,在此基础上进一步进行精细光谱混合分析,迭代光谱混合分析构建端元子集,最终根据重构误差变化量确定各个像元的最优端元集。实验结果表明:相比迭代光谱混合分析法和分层多端元光谱混合分析法,所提出的由粗到细的高光谱图像多端元光谱混合分析能有效降低算法反演丰度误差并改善计算效率。  相似文献   

9.
光谱可变性是影响高光谱图像光谱混合分析精度的重要因素,多端元光谱混合分析是解决该问题的有效手段。为了降低光谱混合分析时间复杂度的同时提高其精度,提出了一种由粗到细的多端元光谱混合分析算法,该算法首先基于扩展的端元集对每个像元进行全约束光谱混合粗分析,确定含所有地物的初始端元集,在此基础上进一步进行精细光谱混合分析,迭代光谱混合分析构建端元子集,最终根据重构误差变化量确定各个像元的最优端元集。实验结果表明:相比迭代光谱混合分析法和分层多端元光谱混合分析法,所提出的由粗到细的高光谱图像多端元光谱混合分析能有效降低算法反演丰度误差并改善计算效率。  相似文献   

10.
Endmember variability in Spectral Mixture Analysis: A review   总被引:9,自引:0,他引:9  
The composite nature of remotely sensed spectral information often masks diagnostic spectral features and hampers the detailed identification and mapping of targeted constituents of the earth's surface. Spectral Mixture Analysis (SMA) is a well established and effective technique to address this mixture problem. SMA models a mixed spectrum as a linear or nonlinear combination of its constituent spectral components or spectral endmembers weighted by their subpixel fractional cover. By model inversion SMA provides subpixel endmember fractions. The lack of ability to account for temporal and spatial variability between and among endmembers has been acknowledged as a major shortcoming of conventional SMA approaches using a linear mixture model with fixed endmembers. Over the past decades numerous efforts have been made to circumvent this issue. This review paper summarizes the available methods and results of endmember variability reduction in SMA. Five basic principles to mitigate endmember variability are identified: (i) the use of multiple endmembers for each component in an iterative mixture analysis cycle, (ii) the selection of a subset of stable spectral features, (iii) the spectral weighting of bands, (iv) spectral signal transformations and (v) the use of radiative transfer models in a mixture analysis. We draw attention to the high complementarities between the different techniques and suggest that an integrated approach is necessary to effectively address endmember variability issues in SMA.  相似文献   

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