基于OSP的端元个数估计方法

针对在缺乏先验知识的情况下难以确定高光谱影像端元个数的问题,提出一种新的虚拟维数估计方法,其结果可作为端元个数的估计。该方法采用正交子空间投影(OSP)原理,逐个提取并剥离端元信号,通过比较残余值与阈值,实现虚拟维数的估计。对模拟高光谱数据和PHI高光谱影像数据的实验结果验证方法的可行性,与Nerman-Pearson法相比其具有更高的灵活性和准确性。     

基于独立分量分析的高光谱图像降维与压缩算法

论文提出了一种基于快速独立分量分析的高光谱图像降维算法.利用虚拟维数算法估计需要保留的独立分量数目,采用非监督端元提取算法自动获取端元矢量,并对快速独立分量分析的混合矩阵进行有效初始化.采用最大噪声分离变换对原始数据进行预处理,利用快速独立分量分析从变换后的主分量中依次提取出各端元对应的独立分量,最后对各个独立分量分别实施无损压缩.实验结果表明,该算法降维后的独立分量具有较好的地物分类性能,并且可以获得较好的压缩性能.     

Fast determination of the number of endmembers for real-time hyperspectral unmixing on GPUs

Spectral unmixing is a very important task for remotely sensed hyperspectral data exploitation. It amounts at identifying a set of spectrally pure components (called endmembers) and their associated per-pixel coverage fractions (called abundances). A challenging problem in spectral unmixing is how to determine the number of endmembers in a given scene. Several automatic techniques exist for this purpose, including the virtual dimensionality (VD) concept or the hyperspectral signal identification by minimum error (HySime). Due to the complexity and high dimensionality of hyperspectral scenes, these techniques are computationally expensive. In this paper, we develop new fast implementations of VD and HySime using commodity graphics processing units. The proposed parallel implementations are validated in terms of accuracy and computational performance, showing significant speedups with regards to optimized serial implementations. The newly developed implementations are integrated in a fully operational unmixing chain which exhibits real-time performance with regards to the time that the hyperspectral instrument takes to collect the image data.     

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。     

基于混合概率PCA模型高光谱图像本征维数确定

如何有效实现降维是现代成像光谱仪辨识地物类别的一个难点所在。该文在已知高光谱图像地物类别数的情况下，提出了一种采用混合最小描述长度(MMDL)模型选择准则确定高光谱图像本征维数的方法。该方法在期望最大化算法框架下同时实现混合PPCA降维和聚类，并根据MMDL准则确定数据降维维数，可以得到数据在概率意义下的精确的降维表征。仿真数据和真实数据进行的比较实验表明，该方法能精确地选择数据的本征维数。     

A real-time unsupervised background extraction-based target detection method for hyperspectral imagery

Target detection is an important technique in hyperspectral image analysis. The high dimensionality of hyperspectral data provides the possibility of deeply mining the information hiding in spectra, and many targets that cannot be visualized by inspection can be detected. But this also brings some problems such as unknown background interferences at the same time. In this way, extracting and taking advantage of the background information in the region of interest becomes a task of great significance. In this paper, we present an unsupervised background extraction-based target detection method, which is called UBETD for short. The proposed UBETD takes advantage of the method of endmember extraction in hyperspectral unmixing, another important technique that can extract representative material signatures from the images. These endmembers represent most of the image information, so they can be reasonably seen as the combination of targets and background signatures. Since the background information is known, algorithm like target-constrained interference-minimized filter could then be introduced to detect the targets while inhibiting the interferences. To meet the rapidly rising demand of real-time processing capabilities, the proposed algorithm is further simplified in computation and implemented on a FPGA board. Experiments with synthetic and real hyperspectral images have been conducted comparing with constrained energy minimization, adaptive coherence/cosine estimator and adaptive matched filter to evaluate the detection and computational performance of our proposed method. The results indicate that UBETD and its hardware implementation RT-UBETD can achieve better performance and are particularly prominent in inhibiting interferences in the background. On the other hand, the hardware implementation of RT-UBETD can complete the target detection processing in far less time than the data acquisition time of hyperspectral sensor like HyMap, which confirms strict real-time processing capability of the proposed system.     

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。     

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.     

高光谱数据分类新方法研究

传统的独立分量分析（ICA）算法无法确定高光谱数据中独立分量的个数,利用概率神经网络（PNN）训练时间短的优点,根据分类精度可以较快地确定出独立分量的个数。提出了一种在确定高光谱数据的维数之后利用支持向量机（SVM）分类的新算法思想,首先利用ICA对高光谱数据降维,并利用PNN确定出独立分量的个数,而后对降维后的数据利用SVM作交叉验证,并采用混合核函数进行分类的算法思想。通过仿真实验表明,该算法可以在保证分类精度的同时大大减少分类的时间。     

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。     

基于同质性降维和CMP算法的高光谱图像分类

针对高光谱数据维数高,波段间冗余信息大的问题,提出一种基于同质性降维和组合匹配追踪算法的高光谱图像分类方法。该方法首先利用均值漂移算法对高光谱图像进行分割得到同质性图像块,对同质性的图像块进行流行学习得到降维映射函数,然后由降维后的高光谱数据训练稀疏最小二乘支持向量机分类模型,为避免正交匹配追踪稀疏重构算法迭代次数多的缺点,提出一种基于组合匹配追踪的稀疏重构求解方法。通过高光谱数据的分类结果可以得出,该方法有效提高了高光谱图像的分类精度。     

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.     

Real-time N-finder processing algorithms for hyperspectral imagery

N-finder algorithm (N-FINDR) is probably one of most popular and widely used algorithms for endmember extraction in hyperspectral imagery. When it comes to practical implementation, four major obstacles need to be overcome. One is the number of endmembers which must be known a priori. A second one is the use of random initial endmembers to initialize N-FINDR, which generally results in different sets of final extracted endmembers. Consequently, the results are inconsistent and not reproducible. A third one is requirement of dimensionality reduction (DR) where different used DR techniques produce different results. Finally yet importantly, it is the very expensive computational cost caused by an exhaustive search for endmembers all together simultaneously. This paper re-designs N-FINDR in a real time processing fashion to cope with these issues. Four versions of Real Time (RT) N-FINDR are developed, RT Iterative N-FINDR (RT IN-FINDR), RT SeQuential N-FINDR (RT SQ N-FINDR), RT Circular N-FINDR, RT SuCcessive N-FINDR (RT SC N-FINDR), each of which has its own merit for implementation. Experimental results demonstrate that real time processing algorithms perform as well as their counterparts with no real-time processing.     

基于粒子群优化的高光谱影像端元提取算法

回顾了粒子群算法的基本原理,分析了端元提取算法的两种技术途径。利用粒子群优化的原理,结合凸面几何学理论和线性光谱混合模型,设计了一种粒子群优化端元提取算法,并设计了算法的快速实现方法。该算法不需要假设影像中存在纯像元,同时保持了端元光谱的形状。利用模拟数据和AVIRIS影像对该算法、SGA算法和NMF算法进行实验对比分析,实验结果证明该算法的端元提取精度优于其他二者。     

FPGA implementation of collaborative representation algorithm for real-time hyperspectral target detection

Hyperspectral image contains various wavelength channels and the corresponding imagery processing requires a computation platform with high performance. Target and anomaly detection on hyperspectral image has been concerned because of its practicality in many real-time detection fields while wider applicability is limited by the computing condition and low processing speed. The field programmable gate arrays (FPGAs) offer the possibility of on-board hyperspectral data processing with high speed, low-power consumption, reconfigurability and radiation tolerance. In this paper, we develop a novel FPGA-based technique for efficient real-time target detection algorithm in hyperspectral images. The collaborative representation is an efficient target detection (CRD) algorithm in hyperspectral imagery, which is directly based on the concept that the target pixels can be approximately represented by its spectral signatures, while the other cannot. To achieve high processing speed on FPGAs platform, the CRD algorithm reduces the dimensionality of hyperspectral image first. The Sherman–Morrison formula is utilized to calculate the matrix inversion to reduce the complexity of overall CRD algorithm. The achieved results demonstrate that the proposed system may obtains shorter processing time of the CRD algorithm than that on 3.40 GHz CPU.     

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。     

基于类解混的高光谱和多光谱图像融合算法

基于解混合的图像融合算法存在的2个问题:(1)用低分辨率高光谱图像(low-resolution hyperspectral image,LR-HSI)的光谱特征重建高分辨率高光谱图像(high-resolution hyperspectral image,HR-HSI),而LR-HSI的空间降质会导致光谱的精度损失;(2)基于非负矩阵解混的算法由于目标函数非凸性,其求解对初始值敏感,导致端元和丰度值不稳定.为解决此问题,提出基于类解混的高光谱图像融合算法.首先,利用模糊c均值算法对图像聚类,以距离聚类中心最近的像素代替解混端元,避开了直接解混导致的解不稳定问题.其次,为每类地物分别学习基于广义回归神经网络(general regression neural network,GRNN)的相同场景HR-HSI和LR-HSI在光谱域的非线性映射关系,弥补由于空间降质导致的端元光谱精度损失.文中借鉴解混合思想,由低分辨率高光谱图像的端元重建高分辨率高光谱图像的端元,将其与高分辨率多光谱图像(high-resolution multispectral image,HR-MSI)的稀疏系数结合得到HR-HSI.在4组数据集上验证本算法性能,与多种融合算法比较.实验表明,Salinas数据的实验结果在SAM,RMSE和ERGAS指标与次优的方法相比,它们的数值分别降低了5.5%,5.5%和1.6%;在Cuprite数据上数值降低了1.3%,3.9%和3.8%;在Indian Pines数据上数值分别降低了1.7%,4.0%和3.9%;在Pavia Center数据上,采用双三次插值时在SAM和ERGAS指标上与次优的方法相比数值分别降低了2.9%和8.5%;采用双线性插值时数值分别降低了3.5%和3.4%.所以,文中算法在有效地提升空间分辨率的同时,很好地保持了光谱信息.     

Implementation and evaluation distributed mixed pixels analysis algorithm for hyperspectral image based on constraint non-negative matrix factorization

As an effective blind source separation method, non-negative matrix factorization has been widely adopted to analyze mixed data in hyperspectral images. To avoid trapping in local optimum, appropriate constraints are added to the objective function of NMF, whose reflections of image essential attribute determine the performance finally. In this paper, a new NMF-based mixed data analysis algorithm is presented, with maximum overall coverage constraint introduced in traditional NMF. The new constraint was proposed using data geometrical properties in the feature space to maximizes the number of pixels contained in the simplex constructed by endmembers compulsorily and introduced in objective function of NMF, named maximum overall coverage constraint NMF (MOCC-NMF), to analyze mixed data in highly mixed hyperspectral data without pure pixels. For implementing easily, multiplicative update rules are applied to avoid step size selection problem occurred in traditional gradient-based optimization algorithm frequently. Furthermore, in order to handle huge computation involved, parallelism implementation of the proposed algorithm using MapReduce is described and the new partitioning strategy to obtain matrix multiplication and determinant value is discussed in detail. In the numerical experiments conducted on real hyperspectral and synthetic datasets of different sizes, the efficiency and scalability of the proposed algorithm are confirmed.     

基于DS聚类的高光谱图像集成分类算法

针对高光谱遥感图像维数高、样本少导致分类精度低的问题,提出一种基于DS聚类的高光谱图像集成分类算法(DSCEA)。首先,根据高光谱数据特点,从整体波段中随机选择一定数量的波段,构成不同的训练样本;其次,分析图像的空谱信息,构造无向加权图,利用优势集(DS)聚类方法得到最大特征差异的波段子集;最后,根据不同样本,利用支持向量机训练具有差异的单个分类器,采用多数表决法集成最终分类器,实现对高光谱遥感图像的分类。在Indian Pines数据集上DSCEA算法的分类精度最高可达到84.61%,在Pavia University数据集上最高可达到91.89%,实验结果表明DSCEA算法可以有效的解决高光谱分类问题。