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。     

一种利用均值匹配改进的高光谱异常检测方法

传统高光谱异常检测算法由于背景信息估计不准确等原因普遍存在高虚警率的问题,针对这一现象,提出了一种利用图像均值进行匹配改进的高光谱异常目标检测后验处理方法。首先采用传统的高光谱异常检测算法将待检测高光谱图像划分为背景与异常目标潜在区域,之后通过对待测图像求解均值,将其与异常目标潜在区域像元进行相似性匹配计算,剔除大范围误检像元,得到最终检测结果。该方法在传统异常目标检测算法基础上进行相似度量剔除大范围虚警像元,在提高原算法探测能力的同时有效地降低虚警率。实验表明,该方法可以有效降低虚警率,提高原算法对于亚像元异常目标的检测能力,且对于不同算法、不同数据具有普适性。     

一种高光谱遥感影像端元自动提取方法

针对人工样本选择和端元提取存在的不确定性和工作量大等缺点,提出一种集成非监督分类、纯净像元指数计算、线性光谱混合模型和凸面单形体理论的自动端元提取算法,能够有效地提取端元用于高光谱遥感影像分类和混合像元分解。利用北京昌平地区的OMIS高光谱遥感数据进行了验证,结果表明算法可行有效,自动化程度较高,作为训练样本进行分类能够获得较高精度,优于常规方法。     

端元约束下的高光谱混合像元非负矩阵分解

提出一种端元约束条件下的非负矩阵分解方法来自动反演混合像元组分。以端元光谱之间的差距为约束条件,使得目标函数综合了影像的分解误差和端元光谱的影响,并以最大后验概率方法导出了限制性非负矩阵分解的迭代算法。成像光谱数据实验结果表明该方法能够自动提取影像的端元光谱矩阵与组分信息,且分解精度比IEA方法高。     

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。     

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。     

粒子群空间优化的端元提取算法

粒子群优化算法（Particle Swarm Optimization,PSO）应用于高光谱影像端元提取时,由于影像中存在端元的像元数所占比例极小且分布零散,导致粒子群的搜索空间破碎,存在收敛性能低、容易陷入局部最优解等缺陷。对粒子群的搜索空间进行优化,选择影像中纯净像元指数（Pixel Purity Index,PPI）较大的像元作为预选像元,然后对预选像元进行光谱聚类排序,将排序后的集合作为粒子群的搜索空间,优化了粒子的搜索空间。并在迭代过程中,充分利用粒子群的信息自适应地调整其系数,在缩小原始图像与反演图像的误差同时,增加体积约束,在提取端元时更好地保持其原有的形状。通过模拟数据和AVIRIS影像的实验表明该算法具有较好端元提取效果。     

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。     

基于有监督模糊C-均值算法的混合像元分解

遥感影像中普遍存在混合像元,混合像元的分解是遥感图像处理的一大难点,同时也是人们研究的热点。使用有监督的模糊C-均值算法对遥感影像的混合像元进行分解。在传统的模糊C-均值算法的基础上结合先验知识引入优化初始聚类中心的方法,结合通过降采样产生的模拟数据、ETM遥感影像和MODIS遥感影像对算法性能进行了实验。结果表明,算法适用于多光谱遥感图像的混合像元分解,是一种简易可行的方法。     

基于形态学和支持向量的遥感图像混合像元分解

为了更好地解决混合像元问题,将自动形态学端元提取方法与支持向量机算法相结合进行混合像元自动分解。首先利用自动形态学端元提取方法寻找影像的纯净端元,此方法基于形态学理论,结合像素的光谱信息和空间信息,可以更精确地提取纯净端元。然后通过支持向量算法得到像元组分,支持向量机后验概率作为地物的组分信息。实验结果证明,这种方法具有很高的混合像元分解精度。     

A new volume formula for a simplex and its application to endmember extraction for hyperspectral image analysis

Based on the geometric properties of a simplex, endmembers can be extracted automatically from a hyperspectral image. To avoid the shortcomings of the N-FINDR algorithm, which requires the dimensions of the data to be one less than the number of endmembers needed, a new volume formula for the simplex without the requirement of dimension reduction is presented here. We demonstrate that the N-FINDR algorithm is a special case of the new method. Moreover, whether the null vector is included as an endmember has an important effect on the final result of the endmember extraction. Finally, we compare the new method with previous methods for endmember extraction of hyperspectral data collected by the Advanced Visible and Infrared Imaging Spectrometer (AVIRIS) over Cuprite, Nevada.     

面向端元提取的粒子群优化遗传算法

现有的粒子群优化(PSO)算法和遗传算法(GA)无法很好地解决高光谱影像端元提取这类离散解空间内的大规模取样优化问题。针对该问题,借鉴凸面几何学理论,利用局部模式粒子群优化的原理改进遗传算法,提出一种面向高光谱影像端元提取的粒子群优化遗传算法(PSOGA)。利用模拟数据和PHI影像对PSOGA算法和GA算法进行实验对比。分析结果证明,PSOGA算法的收敛速度优于GA算法。     

Integration of spatial-spectral information for the improved extraction of endmembers

Spectral-based image endmember extraction methods hinge on the ability to discriminate between pixels based on spectral characteristics alone. Endmembers with distinct spectral features (high spectral contrast) are easy to select, whereas those with minimal unique spectral information (low spectral contrast) are more problematic. Spectral contrast, however, is dependent on the endmember assemblage, such that as the assemblage changes so does the “relative” spectral contrast of each endmember to all other endmembers. It is then possible for an endmember to have low spectral contrast with respect to the full image, but have high spectral contrast within a subset of the image. The spatial-spectral endmember extraction tool (SSEE) works by analyzing a scene in parts (subsets), such that we increase the spectral contrast of low contrast endmembers, thus improving the potential for these endmembers to be selected. The SSEE method comprises three main steps: 1) application of singular value decomposition (SVD) to determine a set of basis vectors that describe most of the spectral variance for subsets of the image; 2) projection of the full image data set onto the locally defined basis vectors to determine a set of candidate endmember pixels; and, 3) imposing spatial constraints for averaging spectrally similar endmembers, allowing for separation of endmembers that are spectrally similar, but spatially independent. The SSEE method is applied to two real hyperspectral data sets to demonstrate the effects of imposing spatial constraints on the selection of endmembers. The results show that the SSEE method is an effective approach to extracting image endmembers. Specific improvements include the extraction of physically meaningful, low contrast endmembers that occupy unique image regions.     

A new non-negative matrix factorization method based on barycentric coordinates for endmember extraction in hyperspectral remote sensing

In this study, we present a new non-negative matrix factorization (NMF) method using the pixel's barycentric coordinates for endmember extraction, named BC-NMF. Our method applies the geometrical property of simplex in the calculation of abundance fraction. That is, for any pixel in an image, its abundance fractions are its barycentric coordinates within the endmember coordinate system. Experiments using both simulated and real hyperspectral images show that BC-NMF can generate endmembers with higher accuracy and lower computational complexity than NMF.     

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.     

基于数据场的端元提取算法研究

传统端元提取算法一般需要人工指定端元数目,易导致多选或漏选端元。利用数据场自然拓扑聚类、可视化的特性,提出了基于数据场的端元提取方法。首先对图像进行分区处理,然后应用数据场思想计算各区域数据点的势能,并分别选择一定数量的特征点,将所有特征点集合成特征图像,再计算特征图像的数据场;最后根据数据场形成的拓扑聚类结构,可视化地提取端元,获得最佳端元的数目和位置。利用Cuprite矿区的AVIRIS数据进行端元提取实验,结果表明:该方法是合理有效的,能够应用于高光谱图像的端元提取中。     

高光谱遥感影像端元提取算法研究进展及分类

在给出端元的物理、代数和几何学解释基础上,对现有端元提取算法从算法设计机理出发,分为基于几何学、基于统计学和信号检测理论以及空间和光谱相结合三大类,并进一步对基于几何学的端元提取算法从技术处理手段差异细分为基于距离、体积、投影变换和最优化4种情况。结合端元提取算法分类,针对算法缺陷及改进思路,介绍了常见端元提取算法PPI、N-FINDR、UOSP、VCA、ICA、NMF和AMEE研究进展。最后,结合解混理论进展和工程应用实际,从技术综合和性能优化的角度指出了端元提取算法的研究展望。     

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.     

A survey of methods incorporating spatial information in image classification and spectral unmixing

Over the past decade, the incorporation of spatial information has drawn increasing attention in multispectral and hyperspectral data analysis. In particular, the property of spatial autocorrelation among pixels has shown great potential for improving understanding of remotely sensed imagery. In this paper, we provide a comprehensive review of the state-of-the-art techniques in incorporating spatial information in image classification and spectral unmixing. For image classification, spatial information is accounted for in the stages of pre-classification, sample selection, classifiers, post-classification, and accuracy assessment. With regards to spectral unmixing, spatial information is discussed in the context of endmember extraction, selection of endmember combinations, and abundance estimation. Finally, a perspective on future research directions for advancing spatial-spectral methods is offered.     

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。