利用归一化光谱混合分析方法研究城市组成

随着城市的快速发展,明确城市生物物理组成和动态变化成为一个重要的研究课题。遥感技术在监测城市组成和时空变化中起到了非常重要的作用。光谱混合分析(SMA)方法为城市环境的描述、城市发展模拟和环境影响分析提供了基础。本文以南京城市为例,结合ETM 遥感影像,利用归一化光谱混合分析方法来监测城市地表类型组成。具体就是通过对反射率进行归一化处理来消除纯净地表覆盖类型的光谱变异,从而提高组分端元的选取精度。然后选取了植被、不透地表和阴影三种组分,对归一化反射率图像使用限制的线性光谱混合分析来研究城市组成。最后对归一化前后的光谱混合分析结果进行了比较,证实了本方法可有效提高光谱混合分析的精度。     

基于Landsat 的城市热特征研究——以兰州市为例

运用Landsat5和Landsat7获得的遥感数据评估兰州市区的热特征,为了量化城市土地利用和覆盖密度,在对植被-非渗透面-土壤(V-I-S) 模型修正的基础上,运用线性光谱混合分析技术(LSMA),分解了不同种类的地表组成并对非渗透面密度进行了分类。结果表明,研究区有很高的热效应,这种热效应与城市开发密度高相关,并通过植被覆盖信息、非渗透面的空间分布及与其相关联的热特征,可以有效量化城市土地利用、开发密度和热格局。     

多光谱蚀变遥感异常提取方法研究

围岩蚀变是热液成矿作用发生过程的一个重要标志。国内外学者为了利用遥感技术提取围岩蚀变信息,开展了多种图像处理方法研究,如主分量阀值分析法、光谱角法、混合像元分解法、高植被覆盖区岩石矿化蚀变信息提取等。本文综述了在地表覆盖类型不同、地表覆盖程度不同的背景下,特别是植被覆盖严重地区的蚀变遥感异常提取的理论依据及异常提取方法研究现状,并对多光谱蚀变遥感异常提取的发展前景提出一些设想。     

多光谱蚀变遥感异常提取方法研究

围岩蚀变是热液成矿作用发生过程的一个重要标志.国内外学者为了利用遥感技术提取围岩蚀变信息,开展了多种图像处理方法研究,如主分量阀值分析法、光谱角法、混合像元分解法、高植被覆盖区矿化蚀变信息提取等.本文综述了在地表覆盖类型不同、地表覆盖程度不同的背景下,特别是植被覆盖严重地区的蚀变遥感异常提取的理论依据及异常提取方法的研究现状,并对多光谱蚀变遥感异常提取的发展前景提出一些设想.     

4种混合像元分解方法在沿海丘陵城市地表组分分析中的比较研究

近年来混合像元分解在城市地表组分监测与分析中的应用逐渐成为城市遥感的一个热点。目前多数研究选择内陆或沿海平原城市作为研究区,并在分解之前把水体作为干扰因素掩膜去除,以获得较好的分解结果,并符合V-I-S(Vegetation-Impervious Surface-Soil)概念模型。以沿海丘陵城市厦门为研究对象,使用4种方法对2007年1月8日TM影像进行混合像元分解,对分解结果的有效性进行了比较,并使用2006年12月25日SPOT5影像对分解结果的精度进行评估和比较。结果表明：在厦门这种沿海丘陵城市,去水的3端元方法会引起阴坡植被与不透水面之间的光谱混淆,而包含水体的4端元方法则能得到更好的分解结果,光谱归一化对于分解结果并没有太大的改善。下一步将通过更多的试验来获得适合沿海丘陵城市的最佳光谱混合像元分解方法。     

一种基于光谱归一化下的植被覆盖度反演算法

针对常规混合像元分解算法在植被覆盖度遥感反演中存在的端元变化误差及运算效率的问题,以两个不同类型植被覆盖下地区的TM影像数据为基础,提出了一种基于光谱归一化框架下的协同稀疏回归的植被覆盖度反演算法,并针对多种地表类型下的植被覆盖度反演试验,与常用的像元二分法模型进行对比分析。试验结果表明:对影像与端元组进行归一化后,有效地降低了它们的异质性,从而提高了反演精度,同时,该算法获取的植被覆盖度相对像元二分法具有更高的精度。     

FUZZY-ARTMAP混合像元分解及其应用

对于定量遥感来说,混合像元是制约它深入发展的难点之一。针对中高分辨率图像ETM＋,采用FUZZY ARTMAP神经网络进行混合像元分解,获得像元中不同地物的含量,进而获得像元的植被覆盖度。该方法在某地区ETM+图像的应用表明,试验结果能满足应用需求。     

基于混合光谱分解的兰州城市热岛与下垫面空间关系分析

利用Landsat ETM+数据,采用混合像元线性光谱分解方法提取的城市植被覆盖度与不透水面表征城市下垫面,通过单窗算法反演地表真实温度,对兰州市中心城区的夏季城市热岛强度与城市下垫面的空间分布关系进行相关分析。结果显示,利用中等分辨率ETM+影像对兰州中心城区不透水面和植被盖度分布提取,其成本较低,精度令人满意;兰州城区植被覆盖、不透水面与热岛强度的分布呈空间正自相关,地表温度的空间依赖性极强,与植被盖度和不透水面在空间方向上的相关性差异较大。     

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。     

Normalized Cut与分水岭变换在高光谱影像混合像元端元提取中的应用

现有的遥感影像端元提取方法主要是从光谱特征角度提出,而结合空间信息的端元提取方法是近些年遥感影像混合像元分解的研究热点,为此使用图论的图像分割Normalized Cut与分水岭变换方法提出了一种改进的空间预处理模型用于高光谱遥感影像混合像元的端元提取。该方法在混合像元端元提取过程中不仅利用遥感影像的光谱信息而且引入了像元的空间位置信息,实验结果表明本文提出的端元提取方法与现有的方法相比提高了遥感影像的混合像元分解精度。     

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.     

基于谐波分析和线性光谱模型的耕地信息提取

耕地是重要的农业资源,如何利用遥感技术快速准确地提取耕地信息是目前研究的热点。利用2000年MODIS/EVI时间序列数据提取关中地区耕地资源信息。以不同地类的EVI时间序列数据年内变化差异为分类依据,采用时间序列谐波分析法对全年时间谱EVI数据进行重构分析,减少噪音对信息提取的影响。经最小噪声分离变换(MNF变换)、纯净像元指数(PPI)计算以及N维可视化工具进行人机交互选取植被、耕地、城镇和水体4种端元,基于线性光谱混合模型,获取该地区耕地资源分布信息。通过与同年1∶10万土地利用数据对比验证,本研究提取的耕地总体精度为83%。研究表明：基于时间序列谐波分析法对EVI数据进行重构,利用不同地类的特征差异,采用混合像元分解的方法,可以精确获取耕地资源定量信息。该方法可为长期、大范围、动态的耕地分布和变化遥感监测提供技术参考,同时为国土资源管理部门提供决策支持。     

Linear spectral unmixing using endmember coexistence rules and spatial correlation

Mixed pixels are often formed when surface materials are smaller than the spatial resolution of a sensor, or two or more ground features fall within a pixel. Spectral unmixing, decomposing a mixed pixel into a set of endmembers and their corresponding abundance fractions, is an important method for extracting the underlying spectral and spatial information from remote sensing images. Recent studies have shown that it is difficult to increase the accuracy of unmixing using single pixel processing. Here, we suggest combining information on the fundamental interrelations of ground components and a priori knowledge on how ground components co-exist or exclude each other according to general geographic and geomorphic relations with spectral information may allow improved unmixing. Therefore, we propose a novel spectral unmixing method to estimate endmember abundances based on linear spectral mixing model with endmember coexistence rules and spatial correlation (LSMM-R&C). This method was implemented by incorporating endmember coexistence rules along with spatial correlation into a weighted least square method. Experiments with both synthetic and real satellite images were carried out to verify the proposed method, and its performance was also evaluated in comparison to the commonly used LSMM (linear spectral mixture method), LAU (local adaptive unmixing), ISU (iterative spectral unmixing) and ISMA (iterative spectral mixture analysis) methods. LSMM-R&C showed the smallest error, and was more effective at revealing the detailed spatial distribution of endmembers’ abundance, showing high potential for solving the problem of spatial heterogeneity among neighbouring pixels.     

基于混合像元分解的喀斯特石漠化地物丰度估测

我国西南喀斯特地区长期存在以石漠化为特征的土地退化问题,是我国三大生态问题之一。喀斯特地区地表复杂度高,具有高度时空异质性,像元混合现象严重,植被、裸岩和裸土为喀斯特地区典型地物,使得评价喀斯特石漠化的关键指标(如裸岩率、植被覆盖度)获取比较困难,高光谱遥感在混合像元分解方面有独特优势,可以获取地物端元的丰度。通过地面试验表明光谱指数能够表征地物覆盖度,进而以Hyperion高光谱影像为数据源,利用连续最大角凸锥方法从影像中提取这3类地物的端元,运用半约束和全约束线性光谱分解方法估算其丰度。研究表明:半约束线性分解得到的丰度优于全约束分解结果,其反演的植被、裸土和裸岩的丰度与相应的光谱指数间具有显著线性相关性,确定系数R²分别为0.92、0.66与0.84,表明地物丰度能够表征其覆盖度。因此,通过混合像元分解算法反演地物丰度来提取喀斯特石漠化因子具有一定的可行性,这为高光谱遥感在喀斯特石漠化中的评价和监测奠定了理论和算法基础。     

A multi-model ensemble approach for quantifying sub-pixel land cover fractions in the urban environments

Land cover information is essential for sustainable management of the environment in urban areas. Satellite images have increasingly been used to extract such information, yet the accuracy has been challenged by the spectral and spatial heterogeneity of urban land covers. This paper presents a framework to develop a more skilful and reliable model for estimating land cover fractions using a multi-model ensemble technique, named Bayesian Model Averaging (BMA). The BMA is a statistical technique that combines the estimates of different models using Bayesian probability theory. In the BMA, each individual estimate is assigned a weight that is optimised in such a way that the likelihood of an individual estimate given the observation is maximised. In this study, three methods, viz. Multi-layer Perceptron (MLP), Pre-screened and Normalised Multiple Endmember Spectral Mixture Analysis (PNMESMA) and Support Vector Regression (SVR) have been used to develop an Ensemble Model (EM). We used a cluster-based approach for applying the BMA to utilise the diverse advantages in individual models. First, the image pixels were separated into three clusters by applying Normalised Difference Vegetation Index (NDVI) thresholds. Second, an ensemble of models for each cluster was derived using the BMA, and these ensembles were finally combined to derive the final output. The EM was tested in a heterogeneous urban area, viz. South Delhi, India, using two multi-spectral images, including Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER). The modelled land cover fractions were compared with the reference land cover fractions derived from a high-resolution (approximately 1 m) panchromatic image of the OrbitView-3 satellite. The accuracy assessment revealed that the EM estimates more accurate and reliable land cover fractions than the individual models on both the images. The performance of the EM in terms of Root Mean Square Error (RMSE), bias and kappa coefficient (k) is generally superior to that of the best of the individual models. These findings can help improve the accuracy of land cover fractions in heterogeneous landscapes by combining the outputs of various diverse models.     

遥感湖泊面积MTMF提取方法及空间尺度效应研究

主要研究遥感湖泊面积亚像元分解提取方法和空间尺度效应,为遥感湖泊面积提取、检验及基于此的局地气候变化分析提供科学的基础数据。在对TM遥感数据进行升尺度处理的基础上,采用混合调制匹配滤波(Mixture Tuned Matched Filtering,MTMF)进行亚像元分解,得到不同空间分辨率的湖泊面积。进而分析不同面积湖泊随遥感空间尺度的变化。结果表明:(1)当通过对高空间分辨率的遥感数据重采样获取多尺度遥感影像进行湖泊面积提取及湖泊空间尺度效应分析时,采用最近邻法比像元聚合重采样法更合理。(2)MTMF亚像元分解法可以用于基于水体光谱特征的遥感湖泊边界提取和面积计算,但边界提取过程中容易将湖泊与河流或其他非湖泊的水体混淆。(3)遥感湖泊面积的提取结果受所用遥感影像空间分辨率的影响较大,影像的空间分辨率越低,湖泊面积提取的偏差越大,尤其对面积较小的湖泊。     

Citrus Recognition Methods of Hyperspectral Remote Sensing Image based on Spectral Unmixing Model

In order to monitor the citrus planting information timely and accurately,We take Huichang County of Jiangxi Province as the research area,using EO\|1 Hypersion hyperspectral remote sensing (HRS)image as a datasource to build a citrus recognition methods of hyperspectral remote sensing image based on spectral unmixing.First of all,the EO\|1 Hyperion hyperspectral remote sensing image has 242 bands,and it has a wide spectrum rang.It can extract the spectral curve of typical objects in the study area,which is based on the image pre\|processing including the band selection,the atmospheric correction and so on.Then,we use the fully constrained linear spectral mixture model of spectral unmixing to decompose the mixed pixels of the image,and then extract the abundance value of citrus.Finally,we construct the relationship between citrus abundance and the actual cultivation of citrus based on the high resolution remote sensing image.The results indicated that the unavoidable error in the extraction of the typical objects and the differences of the citrus canopy coverage can lead to the corresponding relationship between the citrus plant accurate identification and the citrus abundance threshold value.Under the condition of repeated experiments,the study area of citrus abundance thresholds in the range of 0.30~0.45,the overall accuracy can reach more than 90%,and it can meet the requirements of identification of citrus.     

混合像元分解方法的研究

混合像元是遥感领域研究的热点,也是难点。混合像元问题不仅影响地物识别和分类精度,而且是遥感技术定量化发展的重要障碍。为了提高遥感应用的精度,就必须解决混合像元的分解问题。该文首先介绍了混合像元的产生机理,然后阐述了时下几种流行的混合像元分解模型:线性模型、概率模型、几何光学模型、随机几何模型和模糊分析模型,并对其中几种常用模型的优缺点及其适用性进行总结和讨论,对不同模型之间的相似和差异性进行比较分析。     

基于局部连续性与全局相似性的光谱保持型亚像元映射

遥感图像的像元级分类精度受混合像元的影响. 亚像元映射以像元分解获得的丰度值为基础,在地物分布规律的约束下,细化估计各类地物的亚像元级分布模式. 本文同时考虑了地物分布的空间与光谱信息,提出了一种基于局部连续性与全局相似性的光谱保持型亚像元映射算法. 针对地物的空间分布特性,提出了利用类内离散度对局部连续性进行建模,并通过相似分布像元表示误差引入全局相似性约束项. 针对地物的光谱特性,采用最小化光谱误差约束了亚像元映射过程中的光谱无失真性. 模拟数据与真实数据上的实验结果表明,本文算法比其他同类算法具有更高的估计精度,且更适合于实际应用.