基于渐进辐射传输模型的雪粒径与积雪覆盖面积反演算法

渐进辐射传输模型(Asymptotic Radiative Transfer,ART)广泛应用于雪粒径反演,但是ART模型忽略了像元混合的影响。在本文中,ART模型被应用于混合像元分解之中,考虑积雪粒径变化的影响,提出了两种混合像元分解算法,用以同时获取雪粒径和积雪面积。第一种算法是采用均方根误差指标获取每个像元的最优雪粒径,使用全约束线性分解算法(FCLS)反演积雪面积。另一种算法使用通用梯度下降算法(GRG)获取每个像元的最优粒径,仍然采用全约束线性分解算法获取积雪面积。为了提高模型的运行效率,单波段ART算法用来获取影像的雪粒径,将之作为两种算法的先验知识。同时,MODSCAG模型反演的结果也被用于进行交叉对比,实验结果表明,两种算法比MODSCAG模型的反演结果更为精确。     

基于FY-3D/MERSI-Ⅱ的积雪面积比例提取算法北大核心CSCD

风云三号D星(FY-3D)是我国新一代极轨气象卫星。中分辨率光谱成像仪(MERSI-Ⅱ)是其携带的核心传感器之一,FY-3D对于全球数值天气预报、大气定量探测以及气候变化监测等具有重要意义。积雪面积比例产品是众多陆面产品之一,是水文模型和区域气候模型的主要输入参数。基于MERSI-Ⅱ数据发展了业务化提取积雪面积比例的算法,算法核心是混合像元分解。空间光谱端元提取(SSEE)的方法自动提取端元,全约束最小二乘法(FCLS)求解线性混合模型。解混结果叠合云掩膜得到FY-3D/MERSI-Ⅱ积雪面积比例数据(FY-FSC)。以Landsat 8的积雪面积比例数据(L-FSC)作为参考值对FY-FSC进行验证,同时将FY-FSC和MODIS积雪面积比例数据(M-FSC)进行比较。结果表明:FY-FSC的总体相关系数(R)为0.54,均方根误差(RMSE)为0.17,绝对平均误差(AME)为0.10;M-FSC总体R为0.41,RMSE为0.26,AME为0.29;利用积雪面积提取的精度评价因子K比较FY-FSC和M-FSC获取的总积雪面积的精度。结果表明:FYFSC和M-FSC数据的平均K值分别为88.51%和86.78%,FY-FSC精度高于M-FSC。FY-FSC将作为试验参数纳入FY-3D/MERSI-Ⅱ积雪覆盖业务产品中,可填补国产卫星业务化反演积雪面积比例参数的空白。     

基于FY-3D/MERSI-Ⅱ的积雪面积比例提取算法

风云三号D星(FY-3D)是我国新一代极轨气象卫星。中分辨率光谱成像仪(MERSI-Ⅱ)是其携带的核心传感器之一,FY-3D对于全球数值天气预报、大气定量探测以及气候变化监测等具有重要意义。积雪面积比例产品是众多陆面产品之一,是水文模型和区域气候模型的主要输入参数。基于MERSI-Ⅱ数据发展了业务化提取积雪面积比例的算法,算法核心是混合像元分解。空间光谱端元提取(SSEE)的方法自动提取端元,全约束最小二乘法(FCLS)求解线性混合模型。解混结果叠合云掩膜得到FY-3D/MERSI-Ⅱ积雪面积比例数据(FY-FSC)。以Landsat 8的积雪面积比例数据(L-FSC)作为参考值对FY-FSC进行验证,同时将FY-FSC和MODIS积雪面积比例数据(M-FSC)进行比较。结果表明:FY-FSC的总体相关系数(R)为0.54,均方根误差(RMSE)为0.17,绝对平均误差(AME)为0.10;M-FSC总体R为0.41,RMSE为0.26,AME为0.29;利用积雪面积提取的精度评价因子K比较FY-FSC和M-FSC获取的总积雪面积的精度。结果表明:FYFSC和M-FSC数据的平均K值分别为88.51%和86.78%,FY-FSC精度高于M-FSC。FY-FSC将作为试验参数纳入FY-3D/MERSI-Ⅱ积雪覆盖业务产品中,可填补国产卫星业务化反演积雪面积比例参数的空白。     

一种基于噪声水平估计的扩展线性光谱分解算法

针对传统的光谱分解算法忽略了影像在不同波段的不同噪声水平,导致分解精度提高受限。为克服这个问题,以高光谱影像为基础,提出了一种基于噪声水平估计的扩展线性光谱分解算法（NELMM）。首先,根据高光谱应用中的多元回归理论,估计相邻波段的噪声;其次,从估计噪声中获得噪声权重矩阵;最后,将噪声权重矩阵引入到线性混合像元的框架中,可以减轻不同波段噪声水平的影响。为验证算法精度,利用全约束最小二乘法（FCLS）和协同稀疏分解算法（CLSUnSAL）来进行对比分析,并通过此算法反演TM影像的植被覆盖度来验证其在多光谱影像上的实用性。结果表明：NELMM算法对高光谱影像分解的结果比FCLS和CLSUnSAL好,其噪声权重矩阵很好地平衡了波段间的噪声,使NELMM算法分解影像的精度显著提高;同时,此算法对多光谱影像分解呈现很好的适用性。     

基于遗传算法的混合像元快速分解及分类算法

在深入研究混合像元分解原理的基础上,提出了用遗传算法进化模型拟合分解结果的超平面,以实现混合像元分解并进一步分类的算法。给出了用该方法对多光谱图像中混合地物进行分类的实例。实验结果证明:该算法的结果与全约束最小二乘(FCLS)的混合像元分解算法结果相近(相关系数达到0.99),而计算复杂度大大降低,计算速度明显提高且具有较强的适应性。同时在整幅遥感图像的分类中体现出较高的分类性能,为遗传算法在混合地物分类问题中的应用提供了又一条可行的途径。     

由粗到细的高光谱图像多端元光谱混合分析

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。     

由粗到细的高光谱图像多端元光谱混合分析

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。     

由粗到细的高光谱图像多端元光谱混合分析

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。     

用于近红外校正模型的训练样本选择

近红外(near-infrared,NIR)校正模型建立时传统的训练样本选择方法只考虑光谱欧氏距离的同类就近选取,不考虑光谱特征异常的训练样本的影响.基于主元分析(principal component analysis,PCA)残差,在同类就近取样的基础上引入异常光谱剔除技术进行训练样本的二次提取,用于建立偏最小二乘(partial least squares,PLS)回归模型.实验结果表明,该方法比传统方法的预测精度有较明显的提高.     

基于多端元混合光谱模型与Landsat影像的北京不透水层动态研究

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。     

Modelling land‐cover types using multiple endmember spectral mixture analysis in a desert city

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.     

A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper

Spectral matching algorithms can be used for the identification of unknown spectra based on a measure of similarity with one or more known spectra. Two popular spectral matching algorithms use different error metrics and constraints to determine the existence of a spectral match. Multiple endmember spectral mixture analysis (MESMA) is a linear mixing model that uses a root mean square error (RMSE) error metric. Spectral angle mapper (SAM) compares two spectra using a spectral angle error metric. This paper compares two endmember MESMA and SAM using a spectral library containing six land cover classes. RMSE and spectral angle for models within each land cover class were directly compared. The dependence of RMSE on the albedo of the modeled spectrum was also explored. RMSE and spectral angle were found to be closely related, although not equivalent, due to variations in the albedo of the modeled spectra. Error constraints applied to both models resulted in large differences in the number of spectral matches. Using MESMA, the number of spectra modeled within the error constraint increased as the albedo of the modeled spectra decreased. The value of the error constraint used was shown to make a much larger difference in the number of spectra modeled than the choice of spectral matching algorithm.     

Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments

Remote sensing has considerable potential for providing accurate, up-to-date information in urban areas. Urban remote sensing is complicated, however, by very high spectral and spatial complexity. In this paper, Multiple Endmember Spectral Mixture Analysis (MESMA) was applied to map urban land cover using HyMap data acquired over the city of Bonn, Germany. MESMA is well suited for urban environments because it allows the number and types of endmembers to vary on a per-pixel basis, which allows controlling the large spectral variability in these environments. We employed a hierarchical approach, in which MESMA was applied to map four levels of complexity ranging from the simplest level consisting of only two classes, impervious and pervious, to 20 classes that differentiated material composition and plant species. Lower levels of complexity, mapped at the highest accuracies, were used to constrain spatially models at higher levels of complexity, reducing spectral confusion between materials. A spectral library containing 1521 endmembers was created from the HyMap data. Three endmember selection procedures, Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA) and Count Based Endmember Selection (COB), were used to identify the most representative endmembers for each level of complexity. Combined two-, three- or four-endmember models - depending on the hierarchical level - were applied, and the highest endmember fractions were used to assign a land cover class. Classification accuracies of 97.2% were achieved for the two lowest complexity levels, consisting of impervious and pervious classes, and a four class map consisting of vegetation, bare soil, water and built-up. At the next level of complexity, consisting of seven classes including trees, grass, bare soil, river, lakes/basins, road and roof/building, classification accuracies remained high at 81.7% with most classes mapped above 85% accuracy. At the highest level, consisting of 20 land cover classes, a 75.9% classification accuracy was achieved. The ability of MESMA to incorporate within-class spectral variability, combined with a hierarchical approach that uses spatial information from one level to constrain model selection at a higher level of complexity was shown to be particularly well suited for urban environments.     

Spectral mixture analysis for bi-sensor wetland mapping using Landsat TM and Terra MODIS data

Spatial and temporal resolution is essential for understanding the spatial and temporal characteristics and dynamics of wetland ecosystems. However, single satellite imagery with both high spatial resolution and high temporal frequency is currently unavailable. Instead, the development of a bi-sensor monitoring technique utilizing spatial details of middle-to-high resolution data and temporal details of coarse spatial resolution data is highly desirable. For the initial work on our time-series bi-sensor wetland mapping, the applicability of multiple endmember spectral mixture analysis (MESMA) using single-date bi-sensor imagery with different orbiting periods was investigated. Landsat-5 Thematic Mapper (TM) and Terra Moderate Resolution Image Spectrometer (MODIS) data were utilized in the Poyang Lake area in China and the Great Salt Lake area in the USA to examine three decisive elements in utilizing MESMA: (1) the method of optimal endmember selection; (2) the threshold between two- and three-endmember models; and (3) the treatment of shade fractions. As a result, we found that (1) the number of spectra for an endmember spectrum similar to other endmember spectra meeting the modelling restrictions of maximum and minimum land-cover fractions and root mean square error (RMSE) within a class (In_CoB), the number of spectra for an endmember spectrum similar to other endmember spectra meeting the modelling restrictions outside of a class (Out_CoB), the ratio of In_CoB to Out_CoB multiplied by the inverse number of spectra within the class (CoBI) and the endmember average RMSE (EAR) were optimal endmember selection methods for the TM maps, whereas CoBI, EAR and minimum average spectral angle (MASA) were optimal endmember selection methods for the MODIS maps; (2) the MODIS maps were more sensitive to change in the two- and three-endmember modelling thresholds than the TM maps; and (3) the addition of shade fractions to dark water fractions were an appropriate shade treatment. This research demonstrated how MESMA can be applied for multi-scale mapping of wetland ecosystems, how the difference in observation dates between the TM and MODIS data affects the agreement in land-cover fractions and how spectral similarity between dark water and shade affects the agreement in land-cover fractions.     

基于拉格朗日的高光谱解混算法研究*

针对混合像元分解误差问题,提出一种基于拉格朗日算法的高光谱解混算法。通过变分增广拉格朗日算法提取出部分端元,由于端元组中存在相似端元影响解混精度,利用基于梯度的光谱信息散度算法进行光谱区分,除去相似端元。通过对得到的端元进行排序,依次增加端元进行光谱解混,将满足条件的端元增加进端元组,最终得到优选端元。该方法不仅有效去除了相似端元的干扰,而且不需要不断搜索端元的组合,根据每个端元对于混合像元的重要性做出相应次数的非限制性最小二乘法计算,得到更精确高光谱端元的子集,该方法对高光谱混合像元解混的效率以及可靠性均有所提高。     

MESMA与面向对象组合的土地利用分类方法

混合像元是制约传统组合分类方法精度提高的主要因素之一。为此,文章提出一种基于多端元混合像元分解(multiple endmember spectral mixture analysis,MESMA)与面向对象分类组合的分类方法,利用混合像元分解提高分类精度,借助组合方法降低“椒盐”现象影响。首先,使用MESMA技术分解混合像元,提取丰度信息,并将丰度信息作为特征变量应用到像素分类;然后,将像素和面向对象的分类结果进行组合,获得最优分类结果;最后,以内蒙古鄂尔多斯市乌审旗纳林河二号矿井Landsat-8影像为数据源进行相关实验。结果表明:组合方法分类精度最高,与基于像素、面向对象和传统组合方法相比,分类精度分别提高4.56%、5.66%、4.05%;同时,该方法可以有效降低“椒盐”现象影响。     

NASA系列雪参数反演算法在单像元内的时间序列验证与分析

NASA系列算法(Chang,NASA96和Foster算法)是被动微波遥感反演雪深、雪水当量的简单、实用的经验算法,并经过了很多学者大范围的算法验证和改进。为了进一步评价NASA系列算法在东北地区的时空适用性,于长春净月潭区域选定了一个以农田和森林为主的10km×10km被动微波遥感混合像元,在时间上连续观测整个干雪期(2014年12月至次年2月)的积雪参数和气象数据,结合FY3B卫星搭载的微波成像仪(MWRI)亮温数据,对NASA系列算法精度进行了评价分析。结果表明:对于雪深的反演,Chang算法和NASA 96算法前期反演效果较好,后期随着时间的推进高估雪深的趋势愈加明显。由于考虑了森林覆盖率的影响,NASA 96算法的反演精度更高。两种算法最大高估值分别是24.46和14.62cm,这是因为期间雪性质不断变化,尤其是雪粒径不断增大的缘故。Foster算法也严重高估了雪水当量,可能是由于积雪类型的分类系统未必适合于东北地区的积雪特征。本文的积雪连续观测数据为认识东北地区的积雪特性奠定了基础,对算法的时间序列验证与分析为雪参数反演算法的进一步改进提供了可靠依据。