Unsupervised linear spectral mixture analysis with AVIRIS data

A new algorithm for unsupervised hyperspectral data unmixing is investigated, which includes a modified minimum noise fraction （MNF） transformation and independent component analysis （ICA）. The modified MNF transformation is used to reduce noise and remove correlation between neighboring bands. Then the ICA is applied to unmix hyperspectral images, and independent endmembers are obtained from unmixed images by using post-processing which includes image segmentation based on statistical histograms and morphological operations. The experimental results demonstrate that this algorithm can identify endmembers resident in mixed pixels. Meanwhile, the results show the high computational efficiency of the modified MNF transformation. The time consumed by the modified method is almost one fifth of the traditional MNF transformation.

Toward Circumventing Collinearity Effect in Nonlinear Spectral Mixture Analysis by Using a Spectral Shape Measure

Nonlinear spectral mixture analysis (NSMA) is a widely used unmixing algorithm. It can fit the mixed spectra adequately, but collinearity effect among true and virtual endmembers will decrease the retrieval accuracies of endmember fractions. Use of linear spectral mixture analysis (LSMA) can effectively reduce the degree of collinearity in the NSMA. However, the inadequate modeling of mixed spectra in the LSMA will also yield retrieval errors, especially for the cases where the multiple scattering is not ignorable. In this study, a generalized spectral unmixing scheme based on a spectral shape measure, i.e. spectral information divergence (SID), was applied to overcome the limitations of the conventional NSMA and LSMA. Two simulation experiments were undertaken to test the performances of the SID, LSMA and NSMA in the mixture cases of treesoil, tree-concrete and tree-grass. Results demonstrated that the SID yielded higher accuracies than the LSMA for almost all the mixture cases in this study. On the other hand, performances of the SID method were comparable with the NSMA for the tree-soil and tree-grass mixture cases, but significantly better than the NSMA for the tree-concrete mixture case. All the results indicate that the SID method is fairly effective to circumvent collinearity effect within the NSMA, and compensate the inadequate modeling of mixed spectra within the LSMA.

Remote sensing of spatial patterns of urban renewal using linear spectral mixture analysis： A case of central urban area of Shanghai （1997-2000）

It is very important to integrate remote sensing with urban geography that the spectral mix- ture analysis technique is applied to urban land cover evolvement and its eco-environmental effect. Urban land cover is mainly composed of complicated artifi- cial materials, which is the key factor to limit the de- velopment of the spectral mixture analysis technique. There are two main aspects in which the technique of spectral mixture analysis is applied to urban geog- raphy: one is to calculate vegetation fraction; the other is to build quantitative model of the urban im- pervious surface obtained from the combination be- tween high albedo fraction and low albedo fraction. The technique of spectral mixture analysis is firstly applied to study urban renewal pattern, scale and mode which happened in Shanghai City from 1997 to 2000.

Directional Analysis of Urban Expansion Based on Sub-pixel and Regional Scale: A Case Study of Main Districts in Guangzhou, China

Multi-scale data have had a wide-ranging level of performance in the area of urban change monitoring. Herein we investigate the correlation between the impervious surface fraction(ISF) and the Defense Meteorological Satellite Program/Operational Linescan System(DMSP/OLS) nighttime stable light(NTL) data with respect to the urban expansion in the main districts of Guangzhou. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Image(OLI) data from 1988 to 2015 were used to extract the ISF using the linear spectral mixture analysis model and normal difference build-up index at the sub-pixel scale. DMSP/OLS NTL data from 1992 to 2013 were calibrated to illustrate the urban nighttime light conditions at the regional scale. Urban expansion directions were identified by statistics and kernel density analysis for the ISF study area at the sub-pixel scale. In addition, the correlation between the ISF and DMSP/OLS NTL data were illustrated by linear regression analysis. Furthermore, Profile Graph in ArcGIS was employed to illustrate the urban expansion from the differences in correlation in different directions. The conclusions are as follows: 1) The impervious surface(IS)in the study area has expanded to the northeast and the east, starting with the old urban zones, and the high-density IS area has increased by321.14 km^2. 2) The linear regression analysis reveals a positive correlation between the ISF and the DMSP/OLS NTL data. The multi-scale data changes are consistent with the actual urban planning of Guangzhou. 3) The DMSP/OLS NTL data overestimate the urban extent because of its saturation and blooming effects, causing its correlation with ISF to decrease. The pattern of urban expansion influences the saturation and blooming effects of the DMSP/OLS NTL data.

Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution

Impervious surface mapping is essential for urban environmental studies.Spectral Mixture Analysis(SMA)and its extensions are widely employed in impervious surface estimation from medium-resolution images.For SMA,inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors.Meanwhile,the spectral-only SMA,without considering urban spatial distribution,fails to consider spectral variability in an adequate manner.The lack of endmember class diversity and their spatial variations lead to over/underestimation.To mitigate these issues,this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance,taking Wuhan and Wuzhou as two study areas.Specifically,the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarch-ize images into three regions,and distinct endmember combinations are independently developed in each region.Then,spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight.Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE,SE,and RMSE.Further analysis suggests that the hierarch-ical strategy can expand endmember class types and considerably improve the performance for the study areas in general,specifically in less developed areas.Moreover,we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.

多端元模式下高光谱图像解混的不确定性问题

为了探究多端元解混造成解混结果不确定性的原因,并进一步在解混中克服与降低该不确定性,详细分析了在二分类解混中,解混不确定性的两种表现形式,丰度固定时像元位置的不确定性和像元位置固定时丰度的不确定性,探究解混端元与不确定性的相互作用关系,进而提出一种可降低解混丰度不确定性的端元加权多端元解混方法。实验表明:混合丰度不确定性的存在,同时在保证解混精度的前提下验证了所提出的降低不确定性方法的有效性。

面向Sentinel-2影像的亚像元级水体提取方法

结合Sentinel-2影像及其他高分辨率卫星数据进行长序列、高频次、大范围的水面率、蓄水量、生态流量等水资源要素监测具有重要意义。为了提高水体提取精度,解决利用多源中高分辨率卫星数据提取水体时的空间尺度效应问题,本文提出了一种面向Sentinel-2影像的亚像元级水体提取方法(简称SWES)。首先利用RWI提取纯水体像元,然后利用膨胀算法提取水陆边界混合像元,最后为解决地物的类内光谱变化问题,采用考虑空间信息的多端元光谱混合分析算法(MESMA)求解水陆混合像元中的水体丰度。3个试验区的结果均表明,SWES取得了较好效果,平均RMSE为0.147,水体提取效果均优于自动亚像元水体提取方法(简称ASWM),尤其在水陆混合像元较多的坑塘养殖区。SWES在试验区获取的水体面积也有较高精度,平均相对误差为8.03%,低于ASWM的20.23%,结果表明SWES能够有效提升水域面积提取精度。

基于光谱混合分析的毛乌素沙地油蒿群落覆盖度提取

油蒿群落覆盖度是判断毛乌素沙地荒漠化程度严重与否的直接指标,其信息的准确获取有助于更为合理的荒漠化监测与评价。本研究以Landsat ETM+为数据源,深入探讨了光谱混合分析过程中影像预处理、端元选取、光谱混合模型选择及光谱混合分析结果分析等关键问题,给出了恰当的解决方案,进而进行了油蒿群落覆盖度的提取,并通过野外实测数据对提取结果进行了验证。验证结果表明:基于光谱混合分析技术提取的油蒿群落分量与实测油蒿群落覆盖度线性相关显著,相关系数为0.88,因而研究区的油蒿群落覆盖度可以通过油蒿群落分量的线性变换得到。因此,光谱混合分析是提取毛乌素沙地油蒿群落覆盖度的有效技术。

松嫩平原西部土地退化的遥感动态监测研究——以吉林省通榆县为例

以松嫩平原西部土地退化典型地区———吉林省通榆县为例,在RS和GIS集成技术支持下,利用不同时相的TM数据,采用线性光谱混合模型方法,选用植被、盐碱地、裸沙地、沼泽地、苇地作为最终光谱单元进行线性光谱分解,依据盐碱地和沙地加权和分量,将试点区的土地退化划分为未退化、轻度退化、中度退化和重度退化四个等级进行动态监测,并分析土地退化的数量、空间分布与动态过程。

改进的线性混合模型用于高光谱分离实验模拟

从考虑成份光谱的噪声出发 ,提出了改进的线性混合模型来补偿成份光谱噪声的影响 ,并在假定成份光谱和混合光谱噪声源于同一噪声源的前提下 ,给出了改进线性模型的数学求解过程及数值计算思路 ,且通过合成的含噪声的混合光谱对两种方法做了对比验证 ,实验证明 :改进的线性混合模型有效地克服传统线性模型的不足 。

基于MESMA和RF的山丘区土地利用信息分类提取

探讨了基于多端元混合像元分解(Multiple endmember spectral mixture analysis,MESMA)和随机森林(Random forest,RF)相结合的土地利用信息分类提取方法。以Landsat-8 OLI卫星遥感影像为主要数据,基于植被-不透水面-裸土(Vegetation-impervious surface-soil,VIS)模型,利用MESMA将影像分解为植被、不透水面和裸土3类组分,将生成的3类组分变量和基于光谱、纹理信息计算选取的20个特征变量组合后开展RF分类实验,将分类结果与相同特征变量下的支持向量机(Support vector machine,SVM)、最大似然(Maximum likelihood classification,MLC)分类结果进行比较分析。结果表明:MESMA可以获得较为精确的组分丰度信息;RF分类结果优于相同特征变量下的SVM和MLC分类结果;在MESMA生成的组分信息变量参与分类后,3种方法的分类精度均有所改善,分别达90.50%、88.85%、86.35%,其中RF的分类精度改善最为显著;MESMA与线性混合分解(Linear spectral mixture analysis,LSMA)生成的组分信息变量相比,前者对于改善分类精度效果更为明显。MESMA对于提高影像分类精度起到一定积极作用,基于MESMA和RF的方法对中等空间分辨率影像山丘区土地利用信息分类提取精度较高,利用该方法开展遥感影像解译可为大尺度的土地利用监测和管理工作提供技术支持和理论参考。

运用线性光谱混合模型解析城市绿色空间格局：乌鲁木齐市的实证研究

运用线性光谱混合模型并引入植被景观指数解析乌鲁木齐市绿色空间格局及变化特征,为定量评价干旱区绿洲生态环境提供更精确的方法.结果表明:(1)植被盖度与植被指数整体相关性较好,但植被盖度更能反映像元尺度上植被信息较弱的城市植被景观;(2)13年内,植被丰度整体下降明显,存在郊区绿地面积锐减、建成区绿地破碎化程度加剧及空间分布不协调等发展问题;(3)从建成区植被类型看,中、高丰度植被面积缩减最大,低丰度植被面积变化不大但其破碎化程度最大,以低丰度植被占主导的城市绿地结构单一化趋势明显,城市绿色生态系统较不稳定.

棉花遥感识别的混合像元分解

为了进一步提高棉花遥感识别精度,以新疆玛纳斯县为研究区,运用线性光谱混合模型(LSMM),对TM遥感数据的混合像元分解技术与方法进行了研究。将棉花、玉米、番茄和土壤4类典型的端元组分光谱值代入线性模型,在非约束条件下,用最小二乘法估计混合系数,得到每种地物类型的丰度及RMS误差图,以实地测量的棉花种植面积对模型分解效果进行评估,结果表明:线性光谱混合模型构模简单、计算量小,棉花线性光谱混合像元分解精度达到90%以上,可用于新疆棉花的遥感识别。

植被覆盖度提取及景观格局分析

以福建省三明市沙县为例,利用线性光谱混合模型提取植被覆盖度,在对比原始影像构建的四端元模型与加入归一化植被指数的影像构建的五端元模型反演精度的基础上,选择精度更高的五端元模型估算1996、2004、2014年沙县的植被覆盖度,并分析其变化及景观格局。结果表明:沙县植被覆盖结构以高植被覆盖度为主导,高植被覆盖度的区域面积占全县面积的57.87%以上。1996年植被覆盖度最差,1996—2004年,植被覆盖度大幅度上升,增加区域面积比例高达35.15%;2004—2014年,由于城市发展需要,植被覆盖度下降明显,下降区域面积占8.99%,主要分布在沙县中部和北面片区。从植被覆盖度的景观格局来看,极低、低、高植被覆盖度的破碎化程度低,分布较为集中;1996—2014年,极低、低植被覆盖度的破碎化程度先降低后升高,与人类活动有着密切关系。

城市不透水表面遥感估算研究

作为城市化水平的关键指示因子,不透水表面已经被广泛应用在城市生态环境评估中。利用TM影像,采用附有限制条件的线性光谱混合模型对北京城区的不透水表面分布进行空间分析。通过高反照率、低反照率、植被及土壤4类光谱端元的线性组合表征城市土地覆盖类型,综合剔除噪声影响后的高、低反照率分量,估算北京城区不透水表面分布。研究结果表明:利用附有限制条件的线性光谱分解得到的RMS平均值为0.003 428。其不透水表面分布结果与同期spot-5对比验证,R2为0.932,均方根误差为0.086,结果令人满意。

一种新的基于Fisher判别的混合像元分解算法:室内控制实验结果分析

混合像元分解技术(Spectral Mixture Analysis,SMA)是遥感图像处理的重要手段之一.传统方法假设每个端元具有稳定的光谱特征,然而端元内光谱差异普遍存在,这将导致混合像元分解精度的降低.针对该问题,提出了基于Fisher判别(Fisher Discriminant Analysis,FDA)的混合像元分解算法.Fisher判别对光谱各波段进行线性组合,使得转换后的光谱值分离度最大,即端元内的光谱差异较小而端元间的光谱差异较大.利用转换后的光谱对混合像元进行分解可以最大程度地减少端元内光谱差异对分解结果的影响.利用该方法对室内控制实验的模拟混合像元光谱进行分解,并与过去提出的几种混合像元分解技术进行比较,结果显示新方法相比传统方法在分解精度上有相当程度的提高.

兰州市中心城区不透水面覆盖度的遥感估算

利用Landsat 7的ETM+影像,在线性光谱分解的技术上,提取了兰州市中心城区的不透水面分布状况,运用均方根、航片和随机采样对提取精度进行了检验,并对不透水面的空间特征进行了分析.结果显示,利用中等分辨率影像对兰州市中心城区不透水面分布提取的精度较高;植被、高反照度、低反照度和裸露的土壤4种最终光谱端元的线性组合,可以较好地模拟ETM+波谱特征;高反照度、低反照度两种最终光谱端元可以很好地表达城市不透水表面信息.

有氧锻炼对心脏自主神经调节的影响:心率变异性分析结果

为阐明有氧锻炼对心脏自主神经调节功能的影响，以及这种变化与卧位／坐位下体负压（ＬＢＮＰ）作用下的心率调节及立位耐力之间是否有一定联系，用频域、动态谱及非线性指标较全面分析了大学生有氧锻炼６个月前、后心率变异性（ＨＲＶ）的变化。结果表明：常规ＡＲ谱分析的批处理结果只能代表一段时间内ＨＲＶ信号的平均统计特性，方差大，得不出有显著意义的结果。而时变ＡＲ谱则可反映ＬＢＮＰ作用下心迷走撤除及交感激活的动态过程。非线性的β估计得不出有显著意义的结果；但ＡｐＥｎ分析则可敏感地检测出有氧锻炼关联的心率动力学细微变化，且初步揭示ΔＡｐＥｎ与立位耐力变化（ΔＤＮＰ）间显著相关。以上对阐明有氧锻炼对心率动力学调节的影响以及改进ＨＲＶ信号分析工作均有一定意义。

基于不透水地表比例的城市扩展研究

不透水地表比例是衡量城市化水平的一个重要指标,采用不透水地表比例可以研究城市的空间变化和扩展并定量化城市空间变化和扩展的强度。使用多端元光谱混合分析方法可以从TM影像中提取亚像元尺度的不透水地表比例,用该方法从两个时相的TM影像中提取南京都市发展区不透水地表比例,在此基础上定性和定量地分析该区域的空间变化和扩展。

绿洲景观多季相特征的线性光谱混合分解方法研究

因干旱、半干旱区生态环境的脆弱性和社会经济的干扰,区域景观季相间具有显著变异,但该类区域季相景观异质性研究相对薄弱。加上遥感数据空间分辨率的约束,表征荒漠化状态的异质性景观特征以一定程度的混合像元存在,传统的景观信息提取方法具有一定的局限性。以甘肃省民勤县为例,进行了荒漠化异质性季相景观特征的线性光谱混合分解方法研究,包括端元类型确定,多季相代表性端元选择和光谱值优化等关键步骤。结果表明,多季相线性光谱混合分解模型考虑了端元在季相之间的可比性和分解结果的物理意义,可以直接进行物理解译,有效模拟民勤绿洲的景观要素分布,多季相端元丰度可以描述各类端元的季相变异,为土地荒漠化监测评价提供保障。