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。     

基于空谱初始化的非负矩阵光谱混合像元盲分解

混合像元分解是提高遥感监测能力的有效方法之一,因此一直以来是遥感领域的重要研究内容。非负矩阵盲分解(Non-negative Matrix Factorization,NMF)方法无需监督选择端元,无需假定纯像元存在,且能同步获取优化的端元光谱与端元丰度,从而为先验知识不足、高度混合场景下的混合像元分解提供了不错的选择,因此成为高光谱混合像元分解方法的重要分支之一。但NMF易陷入局部最优,若直接应用于混合像元解混难以获取稳定的最优解,从而影响了NMF在光谱混合分解的推广应用。针对这一问题,提出一种利用空谱预处理(SSPP)改进NMF的混合像元分解方法(SSPP-NMF)。首先利用SSPP算法结合空间和光谱信息筛选出合理有效的数据子集;然后用NMF算法对筛选出的数据子集进行混合像元分解,获取具有空间均匀性和光谱纯净性的端元光谱;最后基于上一步获取端元光谱利用非负最小二乘法(NNLS)获取整个研究区的最终端元丰度。为检验该方法的有效性和适用性,分别采用模拟仿真数据和真实遥感影像分析了SSPP对NMF的改善效果,并与ATGP-NMF、MVC-NMF两种基于初始化改进NMF的方法进行了比较分析,结果表明:相比ATGP-NMF、MVC-NMF而言,SSPP算法更能有效抑制噪声的影响,明显地提高NMF分解效果,并且具有较高的时间效率。     

矿物高光谱解混进展研究综述

由于高光谱传感器低空间分辨率特征,岩石高光谱一般是矿物组分的综合反映。矿物高光谱解混对矿产勘查、矿物含量定量反演和野外地质填图等提供了可行的鉴定方法。首先介绍了2种主要的光谱混合模型;其次基于矿物混合机理特征,从模型驱动法和数据驱动法2个方面,对近年高光谱数据的端元提取和丰度求解算法进行归纳,分析各解混算法的原理和优缺点;然后从实验室实测数据、模拟数据和高光谱影像数据3个方面,对目前已开展的混合矿物高光谱解混实验进行概括,总结各算法的解混效果和适用性;最后针对各解混算法的特点和研究现状指出未来矿物高光谱解混的研究方向。     

基于修正MCMC的端元可变的混合像元分解算法

传统的混合像元分解一般是基于固定端元的,然而实际上影像中像元并非都由完全相同的端元组成。基于波谱库,将端元选取和丰度反演合为一个步骤,抽象成一个估计参数的随机过程,在端元数目可变的前提下,基于可逆的跳跃式MCMC方法估计参数,从波谱库中选取端元并对混合像元进行线性解混。在状态转移过程中,加入端元的累积知识,以提高算法效率。这种算法不需要人工干预,能够实现自动化像元分解,并且具有较高的精度。实验表明,基于修正MCMC的端元可变的自动化解混算法在分解精度和稳定性方面均优于基于固定端元的混合像元分解方法。     

基于正则化方法的遥感图像混合像元分解

由于传感器的分辨率的限制,在低空间分辨率遥感图像中存在着大量的混合像元.混合像元所表示的并不是单一地面物体类别的光谱反射值,而是多种类别的反射光谱的组合.混合像元的混合模型可以分为线性混合模型和非线性混合模型.线性混合模型是最常用的一种解混合方法,对于线性混合模型的求解算法进行了研究,根据最小二乘原理,提出了基于正则化方法的线性混合模型求解算法,对实际遥感TM图像进行了解混合运算,求得了端元丰度图像和伪彩色合成图像.     

结合地物光谱库的高光谱端元识别及解混

针对目前基于地物光谱库的高光谱影像稀疏解混方法得到的端元丰度与真实端元丰度仍有较大差距,解混结果中出现很多具有小丰度值的多选端元(伪端元),提出一种基于光谱库的高光谱遥感影像端元识别和稀疏解混方法。首先对影像进行初步稀疏解混,将得到的解混丰度进行显著性分析,自适应地选择显著性丰度阈值,将低于该阈值丰度的端元从混合像元中剔除,得到更为稀疏和准确的表示端元子集。模拟数据的实验表明,该方法能极大提高解混丰度的稀疏性,提高端元识别的准确率,并在一定程度上提高解混的整体精度。真实数据实验结果也验证了该方法在真实影像复杂场景下的有效性。     

利用稳健非负矩阵分解实现无监督高光谱解混

目的 基于非负矩阵分解的高光谱图像无监督解混算法普遍存在着目标函数对噪声敏感、在低信噪比条件下端元提取和丰度估计性能不佳的缺点。因此,提出一种基于稳健非负矩阵分解的高光谱图像混合像元分解算法。方法 首先在传统基于非负矩阵分解的解混算法基础上,对目标函数加以改进,用更加稳健的L₁范数作为重建误差项,提高算法对噪声的适应能力,得到新的无监督解混目标函数。针对新目标函数的非凸特性,利用梯度下降法对端元矩阵和丰度矩阵交替迭代求解,进而完成优化求解,得到端元和丰度估计值。结果 分别利用模拟和真实高光谱数据,对算法性能进行定性和定量分析。在模拟数据集中,将本文算法与具有代表性的5种无监督解混算法进行比较,相比于对比算法中最优者,本文算法在典型信噪比20 dB下,光谱角距离（spectral angle distance,SAD）增大了10.5%,信号重构误差（signal to reconstruction error,SRE）减小了9.3%;在真实数据集中,利用光谱库中的地物光谱特征验证本文算法端元提取质量,并利用真实地物分布定性分析丰度估计结果。结论 提出的基于稳健非负矩阵分解的高光谱无监督解混算法,在低信噪比条件下,能够获得较好的端元提取和丰度估计精度,解混效果更好。     

一种自动获取端元的RMS误差迭代改进算法

端元提取是混合像元分解算法中的关键技术之一,端元的质量直接影响分解结果的精度。本文对基于均方根(RMS)误差分析迭代提取端元的算法进行了改进,提出在端元选择时,增加像元纯净指数(PPI)、光谱矢量距离以及RMS误差值作为约束条件。利用南京地区2002年TM遥感影像作为试验数据,用本文提出的方法提取各组分丰度图,结合V-I-S模型以及研究区的实际情况,分析所提取的各组分丰度空间分布合理性,参考同期IKONOS影像解译结果,对改进前后的分解算法进行精度比较。试验结果表明:基于改进法得到的各组分结果精度较好,其与实测值的回归曲线在相关系数、斜率以及截距方面均得到了较明显的改善,但对于光谱非线性混合现象较严重的地物仍存在一定局限性。     

基于双图正则的半监督NMF混合像元解混算法

在高光谱图像中混合像元普遍存在,这极大地阻碍了高光谱遥感技术的发展进程,因此,在利用光谱图像的过程中,如何准确高效地进行混合像元解混是一个关键问题。对于高光谱图像混合像元分解,使用原始的非负矩阵分解(Nonnegative Matrix Factorization,NMF)算法面临一些困难:首先,其目标函数为非凸函数,难以求解得到全局最优解;其次,混合像元中并不存在纯像元。为了解决这些问题,文中提出一种新的算法——基于双图正则的半监督NMF(Dual graph-regularized Constrained Nonnegative Matrix Factorization,DCNMF)混合像元解混算法。该算法采用了梯度下降法和迭代更新法则,既考虑了高光谱数据流形与光谱特征流形的几何结构,又能跳出局部极值,从而求解得到全局最优解。通过真实的高光谱图像数据仿真实验表明,DCNMF算法能够准确高效地进行混合像元分解,改善了解混效果,提高了解混精度,节约了计算时间,加快了收敛速度。     

基于光谱信息散度与光谱角匹配的高光谱解混算法

针对采用线性逆卷积(LD)算法进行端元初选过程中,端元子集中存在相似端元光谱,影响解混精度的问题,提出了一种基于光谱信息散度(SID)与光谱角匹配(SAM)算法的端元子集优选光谱解混算法。通过在端元进行二次选择时,采用以光谱信息散度和光谱角(SID-SA)混合法准则作为最相似端元选择的判据,去除相似端元,降低相似端元对解混精度的影响。实验结果表明,基于SID与SAM的高光谱解混算法将重构影像的均方根误差(RMSE)降低到0.0104,该方法比传统方法提高了端元的选择精度,减少了丰度估计误差,误差分布更加均匀。     

Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated‐forest hyperspectral data

The spectral unmixing of mixed pixels is a key factor in remote sensing images, especially for hyperspectral imagery. A commonly used approach to spectral unmixing has been linear unmixing. However, the question of whether linear or nonlinear processes dominate spectral signatures of mixed pixels is still an unresolved matter. In this study, we put forward a new nonlinear model for inferring end‐member fractions within hyperspectral scenes. This study focuses on comparing the nonlinear model with a linear model. A detail comparative analysis of the fractions ‘sunlit crown’, ‘sunlit background’ and ‘shadow’ between the two methods was carried out through visualization, and comparing with supervised classification using a database of laboratory simulated‐forest scenes. Our results show that the nonlinear model of spectral unmixing outperforms the linear model, especially in the scenes with translucent crown on a white background. A nonlinear mixture model is needed to account for the multiple scattering between tree crowns and background.     

高光谱遥感图像的单形体分析方法

将n个波段的高光谱图像像元与n维空间里的散点联系起来，结合凸体几何中单形体概念研究高光谱遥感图像纯净像元提取方法，实现图像的地物精确分类识别及像元波谱分解。寻找高光谱遥感图像n维空间里的单形体并认知分析单形体是该研究方法的重要环节。通过MNF(minimum noise fraction)变换和PPI(pixel purity index)计算技术寻找到单形体，基于单形体进行像元分解分析单形体，并结合应用实例和SAM(spectral angle mapper)分类技术完成高光谱图像地物精确分类制图，验证了该研究方法的可操作性。该研究方法的优点在于不需要用户提供地物波谱信息，用于制图和波谱分解的终端单元可由图像本身得到，并由用户控制分类制图和波谱分解的详细程度。     

Kernel sparse representation for hyperspectral unmixing based on high mutual coherence spectral library

ABSTRACT

Sparse regression is now a popular method for hyperspectral unmixing relying on a prior spectral library. However, it is limited by the high mutual coherence spectral library which contains high similarity atoms. In order to improve the accuracy of sparse unmixing with a high mutual coherence spectral library, a new algorithm based on kernel sparse representation unmixing model with total variation constraint is proposed in this paper. By constructing an appropriate kernel function to expand similarity measure scale, library atoms and hyperspectral data are mapped to kernel space where sparse regression algorithms are then applied. Experiments conducted with both simulated and real hyperspectral data sets indicate that the proposed algorithm effectively improves the unmixing performance when using a high mutual coherence spectral library because of its ability to precisely extract endmembers in hyperspectral images. Compared with other state-of-the-art algorithms, the proposed algorithm obtains low reconstruction errors in pixels with different mixed degree.     

Further results on dissimilarity spaces for hyperspectral images RF-CBIR

Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance feedback (RF) is an iterative process that uses machine learning techniques and user’s feedback to improve the CBIR systems performance. We pursued to expand previous research in hyperspectral CBIR systems built on dissimilarity functions defined either on spectral and spatial features extracted by spectral unmixing techniques, or on dictionaries extracted by dictionary-based compressors. These dissimilarity functions were not suitable for direct application in common machine learning techniques. We propose to use a RF general approach based on dissimilarity spaces which is more appropriate for the application of machine learning algorithms to the hyperspectral RF-CBIR. We validate the proposed RF method for hyperspectral CBIR systems over a real hyperspectral dataset.     

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。     

Retrieval of subpixel Tamarix canopy cover from Landsat data along the Forgotten River using linear and nonlinear spectral mixture models

Repeatable approaches for mapping saltcedar (Tamarix spp.) at regional scales, with the ability to detect low density stands, is crucial for the species' effective control and management, as well as for an improved understanding of its current and potential future dynamics. This study had the objective of testing subpixel classification techniques based on linear and nonlinear spectral mixture models in order to identify the best possible classification technique for repeatable mapping of saltcedar canopy cover along the Forgotten River reach of the Rio Grande. The suite of methods tested were meant to represent various levels of constraints imposed in the solution as well as varying levels of classification details (species level and landscape level), sources for endmembers (space-borne multispectral image, airborne hyperspectral image and in situ spectra measurements) and mixture modes (linear and nonlinear). A multiple scattering approximation (MSA) model was proposed as a means to represent canopy (image) reflectance spectra as a nonlinear combination of subcanopy (field) reflectance spectra. The accuracy of subpixel canopy cover was assessed through a 1-m spatial-resolution hyperspectral image and field measurements. Results indicated that: 1) When saltcedar was represented by one single image spectrum (endmember), the unconstrained linear spectral unmixing with post-classification normalization produced comparable accuracy (OA = 72%) to those delivered by partially and fully constrained linear spectral unmixing (63-72%) and even by nonlinear spectral unmixing (73%). 2) The accuracy of the fully constrained linear spectral unmixing method increased (from 67% to 77%) when the classes were represented with several image spectra. 3) Saltcedar canopy reflectance showed the strongest nonlinear relationship with respect to subcanopy reflectance, as indicated through a range of estimated canopy recollision probabilities. 4) Despite the considerations of these effects on canopy reflectance, the inversion of the nonlinear spectral mixing model with subcanopy reflectance (field) measurements yielded slightly lower accuracy (73%) than the linear counterpart (77%). Implications of these results for region-wide monitoring of saltcedar invasion are also discussed.     

Unmixing-based content retrieval system for remotely sensed hyperspectral imagery on GPUs

This paper presents a new unmixing-based retrieval system for remotely sensed hyperspectral imagery. The need for this kind of system is justified by the exponential growth in the volume and number of remotely sensed data sets from the surface of the Earth. This is particularly the case for hyperspectral images, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels. To deal with the high computational cost of extracting the spectral information needed to catalog new hyperspectral images in our system, we resort to efficient implementations of spectral unmixing algorithms on commodity graphics processing units (GPUs). Spectral unmixing is a very popular approach for interpreting hyperspectral data with sub-pixel precision. This paper particularly focuses on the design of the proposed framework as a web service, as well as on the efficient implementation of the system on GPUs. In addition, we present a comparison of spectral unmixing algorithms available in the system on both CPU and GPU architectures.     

基于线性混合模型的高光谱图像端元提取

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。     

一种基于协同稀疏和全变差的高光谱线性解混方法

稀疏分解是高光谱图像（Hyperspectral image,HSI）解混中的常用方法,为了克服传统稀疏解混方法只重视挖掘空间相关性而忽视稀疏性精确刻画的缺点,本文提出一种新的基于协同稀疏和全变差（Total variation,TV）相结合的高光谱空谱联合线性解混方法,从而进一步提高解混的精度.该方法基于已知光谱库的高光谱稀疏线性回归模型,利用TV正则项对高光谱邻域像元间的相关性进行约束;同时,协同稀疏性被用来刻画丰度系数的行稀疏性,从而表明协同稀疏先验对空谱联合解混精度的提高至关重要;最后采用交替方向乘子法求解模型.模拟高光谱数据实验结果定量地验证本文方法能够比现有同类方法获得更精确的解混结果,同时真实高光谱数据实验结果定性地验证了本文方法的有效性.     

Spatial-Spectral Preprocessing based on Nonnegative Matrix Factorization to Unmix Hyperspectral Data

Non-negative Matrix Factorization (NMF)method of blind spectral unmixing can obtain the spectrum and abundance of the endmember by synchronous optimization,without supervising the selection of endmember.Therefore,NMF has been developed rapidly in the application of hyperspectral unmixing.However,traditional blind spectral unmixing NMF method tends to fall into the local optimum and it is difficult to obtain a stable optimal solution.In this paper,we propose an improved Non-negative Matrix Factorization (NMF)method based on Spatial\|Spectal Preprocessing for spectral unmixing of hyperspectral data (SSPP-NMF).First,the SSPP algorithm is used to combine spatial and spectral information to select reasonable and effective dataset.Then,the NMF algorithm is used to unmix this dataset to obtain the final optimized endmember spectrum.Finally,the Non\|Negative Least Squares (NNLS)method is used to obtain the final abundance of the whole study area.The validity and applicability of the proposed method were analyzed based on a set of synthetic hyperspectral data and real hyperspectral images;and then the results were compared with that from three algorithms including the existing NMF algorithm,MVC\|NMF algorithm and ATGP-NMF algorithm.Results show that compared with ATGP-NMF and MVC-NMF,the SSPP algorithm can effectively suppress the influence of noise,significantly improve the performance of the NMF method of blind spectral unmixing algorithm.