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。     

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。     

Modified N-FINDR endmember extraction algorithm for remote-sensing imagery

The N-FINDR, developed by Winter, is one of the most widely used algorithms for endmember extraction for hyperspectral images. N-FINDR usually needs an outer loop to control the stopping rule and two inner loops for pixel replacement, so it suffers from computational inefficiency, particularly when the size of the remote-sensing image is large. Recently, geometric unmixing using a barycentric coordinate has become a popular research field in hyperspectral remote sensing. According to Cramer’s rule, a barycentric coordinate estimated by the ratios of simplex volumes is equivalent to a least-squares solution of a linear mixture model. This property implies a brand new strategy for endmember extraction. In other words, we can deduce endmembers by comparison only of abundances derived from a least-squares approach rather than a complicated volume comparison in N-FINDR. Theoretical analysis shows that the proposed method has the same performance as N-FINDR but with much lower computational complexity. In the experiment using real hyperspectral data, our method outperforms several other N-FINDR-based methods in terms of computing times.     

基于ANN端元估计的高光谱图像解混算法

针对高光谱图像解混问题进行研究,发现传统解混算法在保持端元数目不变的情况下,得到的解混精度不高。为此,基于人工神经网络（ANN）提出一种估计单像素点中端元数目和类别的解混算法。首先利用人工神经网络对遥感图像中各个像素的端元数目和类别进行估计;之后依据估计结果确定解混算法的目标函数,并引入改进的差分搜索算法对目标函数进行优化求解;最终获取地物丰度和待求参数,实现高光谱图像的解混。仿真数据和真实遥感数据实验表明,与现有的解混算法相比,所提解混算法具有更高的解混性能,更加符合实际场景的情况。     

结合空间光谱预处理和约束非负矩阵分解的高光谱图像混合像元分解

目的 混合像元问题在高光谱遥感图像处理分析中普遍存在,非负矩阵分解的方法被引入到高光谱图像解混中。本文提出结合空间光谱预处理和约束非负矩阵分解的混合像元分解流程。方法 结合空间光谱预处理的约束非负矩阵分解,如最小体积约束、流行约束等,通过加入邻域的空间和光谱信息进行预处理获得更优的预选端元,从而对非负矩阵分解的解混结果进行优化。结果 在5组不同信噪比的模拟数据实验中,空间预处理（SPP）和空间光谱预处理（SSPP）均能够有效提高约束非负矩阵分解（最小体积约束的非负矩阵分解和图正则非负矩阵分解）的解混结果,其中SPP在不同信噪比的情况下都能优化约束非负矩阵分解的结果,而SSPP在低信噪比的情况下,预处理效果更佳。利用美国内华达州Cuprite矿区数据进行真实数据实验,SPP提高了约束非负矩阵分解的解混精度,而SSPP在复杂场景下,解混精度更佳。模拟数据和真实数据的实验均表明,空间光谱预处理能够有效地提高约束非负矩阵分解的解混精度,特别是对于信噪比较低的情况下,融合空间和光谱信息对噪声有很好的鲁棒性。结论 本文对约束非负矩阵分解的解混算法添加空间光谱预处理,利用高光谱遥感数据的空间和光谱信息,优化预选端元,加入空间光谱预处理的非负矩阵解混实验流程,在复杂场景情况下,对噪声具有较好的鲁棒性。     

基于异常探测的高光谱端元提取方法研究

端元提取是高光谱影像分析重要且具有挑战性的任务,是解决高光谱图像混合像元分解关键的步骤。现行的高光谱端元提取算法在端元提取过程中,异常像元同时加入到端元数组中,如何有效区分异常与端元,成为高光谱遥感端元提取的瓶颈,也是提高高光谱图像混合像元分解精度的关键因素。提出一种基于异常探测的高光谱端元提取方法,首先利用RX算法对原始影像进行异常探测,根据异常探测的结果剔除一定数量的像元,将剔除的像元用原始图像均值向量替代,再对影像进行正交子空间投影(OSP)提取端元。实验表明,该方法能够有效区分异常与端元,抑制异常像元参与端元提取,同时处理后的图像端元提取的结果受异常处理的影响很小,证明了去除异常信息后提取端元的可行性。     

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。     

Piecewise Convex Mulutiple-model Hyperspectral Imagery ENDmember\|extraction based on Discrete Particle Swarm Optimization

Piecewise COnvex Multiple\|Model ENDmember(PCOMMEND) spectral unmixing can well solve unmixing of the nonconvex hyperspectral data，which improves the calculation accuracy of the standard linear mixed model based on the convex geometry model.the number of piecewise convex is not sure in the practical application，which limits the calculation ccuracy of unmixing and the wrong endmembers will sometimes extracted，in view of the situation，the Discrete Particle Swarm Optimization(D\|PSO)is proposed to unmix the piecewise convex mulutiple\|model hyperspectral imagery，D\|PSO is the intelligent algorithm of random search，and is able to find the global optimal solution of convex function，which reduce the unmixing error caused by the uncertainty number of the convex section，experiments on the simulative data and real data has indicate D\|PSO improves the accuracy of the extracting endmember and estimating the proportion.     

An endmember-based distance for content based hyperspectral image retrieval

We propose a specific content-based image retrieval (CBIR) system for hyperspectral images exploiting its rich spectral information. The CBIR image features are the endmember signatures obtained from the image data by endmember induction algorithms (EIAs). Endmembers correspond to the elementary materials in the scene, so that the pixel spectra can be decomposed into a linear combination of endmember signatures. EIA search for points in the high dimensional space of pixel spectra defining a convex polytope, often a simplex, covering the image data. This paper introduces a dissimilarity measure between hyperspectral images computed over the image induced endmembers, proving that it complies with the axioms of a distance. We provide a comparative discussion of dissimilarity functions, and quantitative evaluation of their relative performances on a large collection of synthetic hyperspectral images, and on a dataset extracted from a real hyperspectral image. Alternative dissimilarity functions considered are the Hausdorff distance and robust variations of it. We assess the CBIR performance sensitivity to changes in the distance between endmembers, the EIA employed, and some other conditions. The proposed hyperspectral image distance improves over the alternative dissimilarities in all quantitative performance measures. The visual results of the CBIR on the real image data demonstrate its usefulness for practical applications.     

基于RM S 误差分析的高光谱图像自动端元提取算法

提出了一种基于RM S ( root mean square) 误差分析的自动端元提取算法。对图像每做一次线性解混合, 就得到一幅以均方根RMS误差表示的残余误差图像, 从中选出误差较大的像素作为新的端元开始下一次解混合, 通过多次迭代, 直到得到了要求数目的端元。该算法克服了以往端元提取方法监督特性的局限, 减少了对先验信息的依赖, 同时保留了图像中的异常。利用仿真和实验数据验证了该算法的有效性。     

一种端元可变的混合像元分解方法

混合像元线性分解是高光谱影像处理的常用方法，它使用相同的端元矩阵对像元进行分解，其结果是分解精度不高。为此提出了一种端元可变的混合像元分解方法，在确定端元矩阵时，首先考察混合像元与端元的光谱相似性，结合地物空间分布特点，实现了可变端元的混合像元分解。试验结果表明，该分解方法分解精度优于传统线性模型，符合实际情况。     

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.     

非监督正交子空间投影的高光谱混合像元自动分解

利用混合像元线性分解技术处理高光谱影像，以获取研究区域中同一像元的不同组份是遥感应用的主要目的之一。近年来，研究者们发展了一种正交子空间投影技术(0SP)，用来探测感兴趣目标，进一步可以用来分解混合像元，然而应用这种方法分解混合像元的缺陷是需要有研究区域的先验信息，这就制约了它在这方面的应用。为此针对这种不足，提出一种非监督的正交子空间投影(UOSP)技术，用来自动获取影像端元光谱，同时进行混合像元分解。并用成像光谱数据(PHI)实例测试了这个方法，结果表明该方法自动获取的端元比较合理，且分解混合像元精度较高。     

A quantitative and comparative analysis of different preprocessing implementations of DPSO: a robust endmember extraction algorithm

Linear spectral unmixing is a very important technique in hyperspectral image analysis. It contains two main steps. First, it finds spectrally unique signatures of pure ground components (called endmembers); second, it estimates their corresponding fractional abundances in each pixel. Recently, a discrete particle swarm optimization (DPSO) algorithm was introduced to accurately extract endmembers with high optimal performance. However, because of its limited feasible solution space, DPSO necessarily needs a small amount of candidate endmembers before extraction. Consequently, how to provide a suitable candidate endmember set, which has not been analyzed yet, is a critical issue in using DPSO for unmixing problem. In this study, three representative pure pixel-based methods, pixel purity index, vertex component analysis (VCA), and N-FINDR, are quantitatively compared to provide candidate endmembers for DPSO. The experiments with synthetic and real hyperspectral images indicate that VCA is the most reliable preprocessing implementation for DPSO. Further, it can be concluded that DPSO with the proposed preprocessing implementations given in this paper is robust for endmember extraction.     

Spatial–spectral preprocessing for spectral unmixing

Most techniques available in the endmember extraction rely on exploiting the spectral information of the data alone. In this paper, we improve the utilization of data information by dividing a pixel into four subpixels which are redefined by the scalar factor related to the spatial–spectral similarity. The spatial information is integrated into the spectral information in a certain spatial neighbourhood domain, which can make extracted endmembers more precisely, because the effect of noise and outliers can be suppressed with preprocessing (PP). Meanwhile, the accuracy of spectral unmixing will be improved without modification to the conventional methods applied to spectral-based endmember extraction. Experimental results with both synthetic and real hyperspectral images demonstrate the unmixing accuracy is better than that without PP.     

马尔可夫随机场的空间相关模型在非负矩阵分解线性解混中的应用

针对基于非负矩阵分解（NMF）的高光谱解混存在的初始化与"局部极小"等问题,提出一种基于马尔可夫随机场（MRF）的空间相关约束NMF线性解混算法（MRF-NMF）。首先,通过基于最小误差的高光谱信号识别（HySime）法估算端元数量,同时利用顶点成分分析（VCA）和全约束最小二乘法（FCLS）初始化端元矩阵与丰度矩阵;其次,利用MRF模型建立描述地物空间分布规律的能量函数,以此描述地物分布的空间相关特征;最后,将基于MRF的空间相关约束函数与NMF标准目标函数以交替迭代的形式参与解混,得出高光谱数据的端元信息与丰度分解结果。理论分析和真实数据实验结果表明,在高光谱数据空间相关程度较低的情况下,相比最小体积约束的NMF （MVC-NMF）、分段平滑和稀疏约束的NMF （PSNMFSC）和交互投影子梯度非负矩阵分解（APS-NMF）三种参考算法,所提算法的端元分解精度仍分别提高了7.82%、12.4%和10.1%,其丰度分解精度仍分别提高了8.34%、12.6%和9.87%。MRF-NMF能够弥补NMF对于空间相关特征描述能力的不足,减小解混结果中地物的空间能量分布误差。     

A new approach based on orthogonal bases of data space to decomposition of mixed pixels for hyperspectral imagery

A new algorithm for decomposition of mixed pixels based on orthogonal bases of data space is proposed in this paper. It is a simplex-based method which extracts endmembers sequentially using computations of largest simplex volumes. At each searching step of this extraction algorithm, searching for the simplex with the largest volume is equivalent to searching for a new orthogonal basis which has the largest norm. The new endmember corresponds to the new basis with the largest norm. This algorithm runs very fast and can also avoid the dilemma in traditional simplex-based endmember extraction algorithms, such as N-FINDR, that it generally produces different sets of final endmembers if different initial conditions are used. Moreover, with this set of orthogonal bases, the proposed algorithm can also determine the proper number of endmembers and finish the unmixing of the original images which the traditional simplex-based algorithms cannot do by themselves. Experimental results of both artificial simulated images and practical remote sensing images demonstrate the algorithm proposed in this paper is a fast and accurate algorithm for the decomposition of mixed pixels.     

基于分块PCA与端元提取的壁画线条增强研究

线状特征是壁画中的重要元素。然而受到自然及人为因素的影响,壁画的部分线条常常变得模糊,人眼难以辨别。因此,提出一种利用高光谱影像分块主成分分析(PCA)与端元提取相结合的线状特征增强方法。首先,利用支持向量机(SVM)对壁画的合成真彩色影像进行分类,根据分类结果得到壁画标签数据,实现高光谱影像同质区域的分块数据。其次,对各分块影像进行顶点成分分析(VCA)得到候选端元集,通过构造投影矩阵合并相似端元确定最终端元集。然后,利用非负最小二乘算法解混得到线条丰度图。最后,将分块PCA的第一主成分影像归一化后与线条丰度图进行波段加权平均获取线状特征增强影像,将其与合成真彩色影像进行HSV图像融合得到线状特征融合影像。以瞿昙寺壁画局部高光谱影像为例进行了验证,结果表明,该算法能增强壁画中的线状特征,且较PCA增强法效果更好。     

Spectral mixture analyses of hyperspectral data acquired using a tethered balloon

Tethered balloon remote sensing platforms can be used to study radiometric issues in terrestrial ecosystems by effectively bridging the spatial gap between measurements made on the ground and those acquired via airplane or satellite. In this study, the Short Wave Aerostat-Mounted Imager (SWAMI) tethered balloon-mounted platform was utilized to evaluate linear and nonlinear spectral mixture analysis (SMA) for a grassland-conifer forest ecotone during the summer of 2003. Hyperspectral measurement of a 74-m diameter ground instantaneous field of view (GIFOV) attained by the SWAMI was studied. Hyperspectral spectra of four common endmembers, bare soil, grass, tree, and shadow, were collected in situ, and images captured via video camera were interpreted into accurate areal ground cover fractions for evaluating the mixture models. The comparison between the SWAMI spectrum and the spectrum derived by combining in situ spectral data with video-derived areal fractions indicated that nonlinear effects occurred in the near infrared (NIR) region, while nonlinear influences were minimal in the visible region. The evaluation of hyperspectral and multispectral mixture models indicated that nonlinear mixture model-derived areal fractions were sensitive to the model input data, while the linear mixture model performed more stably. Areal fractions of bare soil were overestimated in all models due to the increased radiance of bare soil resulting from side scattering of NIR radiation by adjacent grass and trees. Unmixing errors occurred mainly due to multiple scattering as well as close endmember spectral correlation. In addition, though an apparent endmember assemblage could be derived using linear approaches to yield low residual error, the tree and shade endmember fractions calculated using this technique were erroneous and therefore separate treatment of endmembers subject to high amounts of multiple scattering (i.e. shadows and trees) must be done with caution. Including the short wave infrared (SWIR) region in the hyperspectral and multispectral endmember data significantly reduced the Pearson correlation coefficient values among endmember spectra. Therefore, combination of visible, NIR, and SWIR information is likely to further improve the utility of SMA in understanding ecosystem structure and function and may help narrow uncertainties when utilizing remotely sensed data to extrapolate trace glas flux measurements from the canopy scale to the landscape scale.