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.     

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.     

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.     

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。     

Citrus Recognition Methods of Hyperspectral Remote Sensing Image based on Spectral Unmixing Model

In order to monitor the citrus planting information timely and accurately,We take Huichang County of Jiangxi Province as the research area,using EO\|1 Hypersion hyperspectral remote sensing (HRS)image as a datasource to build a citrus recognition methods of hyperspectral remote sensing image based on spectral unmixing.First of all,the EO\|1 Hyperion hyperspectral remote sensing image has 242 bands,and it has a wide spectrum rang.It can extract the spectral curve of typical objects in the study area,which is based on the image pre\|processing including the band selection,the atmospheric correction and so on.Then,we use the fully constrained linear spectral mixture model of spectral unmixing to decompose the mixed pixels of the image,and then extract the abundance value of citrus.Finally,we construct the relationship between citrus abundance and the actual cultivation of citrus based on the high resolution remote sensing image.The results indicated that the unavoidable error in the extraction of the typical objects and the differences of the citrus canopy coverage can lead to the corresponding relationship between the citrus plant accurate identification and the citrus abundance threshold value.Under the condition of repeated experiments,the study area of citrus abundance thresholds in the range of 0.30~0.45,the overall accuracy can reach more than 90%,and it can meet the requirements of identification of citrus.     

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。     

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。     

基于超像素的流形正则化稀疏约束NMF混合像元分解算法

针对传统非负矩阵分解（NMF）法用于高光谱图像混合像元分解时产生的分解结果精度不高、对噪声敏感等问题，提出一种基于超像素的流形正则化稀疏约束NMF混合像元分解算法——MRS-NMF。首先，通过基于熵率的超像素分割来构造高光谱图像的流形结构，把原图像分割为k个超像素块并把每个超像素块中具有相似性质的数据点标上相同的标签，定义像素块内有相同标签的任意两个数据点之间的权重矩阵，然后将权重矩阵应用于NMF的目标函数中以构造出流形正则化约束项；第二，在目标函数中添加二次抛物线函数以完成稀疏约束；最后，采用乘法迭代更新法则求解目标函数以得到端元矩阵和丰度矩阵的求解公式，同时设置最大迭代次数和容忍误差阈值，迭代运算得到最终结果。该方法有效利用了高光谱图像的光谱和空间信息。实验结果表明，在模拟的高光谱数据中，与传统的流形稀疏约束的非负矩阵分解（GLNMF）、L1/2-NMF和顶点成分分析-全约束最小二乘法（VCA-FCLS）等方法相比，MRS-NMF可以提高0.016~0.063的端元分解精度和0.01~0.05的丰度分解精度；而在真实的高光谱图像中，MRS-NMF较传统的GLNMF、顶点成分分析法（VCA）、最小体积约束的非负矩阵分解（MVCNMF）等方法可以平均提高0.001~0.0437的端元分解精度。所提MRS-NMF算法有效地提高了混合像元分解的精度，同时具有较好的抗噪性能。     

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。     

Comparison between Two Spectral Mixture Analysis Methods based on IDL and MATLAB

Interactive Data Language (IDL) is a language in the development of application based on multiplatform and object\|oriented,which has significant advantages in data analysis and visualization.The MATLAB is a software with powerful features in the image of processing and programming in complex numerical analysis,which based on matrix calculation.Programming in combining the IDL with MATLAB,meanwhile using the Extended Linear Mixed Model for endmember unmixing in Hyperion images.To verify the results of endmember unmixing,adopted the Fully Constrained Least Squares for comparative analysis.The results showed that：The method of programming in combining the IDL with MATLAB not noly possess the advantages of both but also enhance the efficiency in programming,it is conducive to remote sensing image processing.Meanwhile endmember unmixing results show that：the Extended Linear Mixing Model unmixing has a higher accuracy when the proportion of local category in the image is large.On the contrary,the Fully Constrained Least Squares unmixing has a higher accuracy.     

一种用于高光谱遥感影像分类的改进多类支持向量机

将支持向量机(SVM)用于高光谱遥感影像分类的研究,采用决策边界特征提取(DBFE)算法对高光谱影像进行维数约简,以径向基函数(RBF)作为SVM模型的核函数,把混沌优化搜索技术引入到PSO算法中,以基本PSO算法为主体流程,对种群中最好的粒子进行给定步数的混沌优化搜索,以改进基本PSO算法进化后期收敛速度慢、易陷入局部极小值的缺陷。利用改进的混合粒子群优化算法(PSO)来实现SVM模型参数的自动选择,继而构建了一种参数最优的粒子群优化支持向量机(PSO-SVM)多类分类模型。选用220波段的AVIRIS高光谱遥感影像进行了分类试验。结果表明,与采用基于留一法(LOO)网格搜索策略的传统SVM相比,改进后的PSO-SVM算法可以提高分类精度约8.8%。该方法对于小样本、非均衡条件下的遥感影像数据分类非常有效。     

Generic visual analysis for multi- and hyperspectral image data

Multi- and hyperspectral imaging and data analysis has been investigated in the last decades in the context of various fields of application like remote sensing or microscopic spectroscopy. However, recent developments in sensor technology and a growing number of application areas require a more generic view on data analysis, that clearly expands the current, domain-specific approaches. In this context, we address the problem of interactive exploration of multi- and hyperspectral data, consisting of (semi-)automatic data analysis and scientific visualization in a comprehensive fashion. In this paper, we propose an approach that enables a generic interactive exploration and easy segmentation of multi- and hyperspectral data, based on characterizing spectra of an individual dataset, the so-called endmembers. Using the concepts of existing endmember extraction algorithms, we derive a visual analysis system, where the characteristic spectra initially identified serve as input to interactively tailor a problem-specific visual analysis by means of visual exploration. An optional outlier detection improves the robustness of the endmember detection and analysis. An adequate system feedback of the costly unmixing procedure for the spectral data with respect to the current set of endmembers is ensured by a novel technique for progressive unmixing and view update which is applied at user modification. The progressive unmixing is based on an efficient prediction scheme applied to previous unmixing results. We present a detailed evaluation of our system in terms of confocal Raman microscopy, common multispectral imaging and remote sensing.     

一种基于多粒子群协同进化的高光谱图像波段选择与分类方法

针对高光谱图像分类过程中数据波段多以及信息冗余量大引起的处理速度慢及Hughes现象等问题,提出了一种基于多粒子协同进化算法进行高光谱图像自动波段选择与分类的方法:使用多粒子群协同进化算法搜索特征子集,对粒子群优化算法进行改进,定义新的位置和速度的更新策略,并以支持向量机为分类器,同时对特征子集和SVM核函数参数进行优化。在协同搜索过程中,引入遗传算法改善粒子群优化的"早熟"收敛问题,构建了一种新的MPSO-SVM(Multiple particle swarm optimization-SVM)分类模型。对高光谱遥感图像的实验结果表明:MPSO-SVM方法不仅能有效地压缩光谱的特征维数,得到最佳的波段组合,还能得到最优的SVM参数,达到较好的分类效果,提高分类精度。     

盲信号分离技术在高光谱混合像元分解中的应用

盲信号分离(BSS)是现代信号处理的一种前沿基础技术,近年来在高光谱混合像元分解领域展示了很好的应用前景。通过比较系统地介绍独立成分分析(ICA)、非负矩阵分解(NMF)、复杂度分析(CA)和稀疏成分分析(SCA) 4种BSS方法的基本原理、基本概念及数学模型,重点阐述4种方法在高光谱混合像元分解中的应用现状及各自的优缺点,旨在进一步探讨BSS技术应用于高光谱混合像元分解面临的挑战与存在的潜力。     

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.     

Research on Using End-member Abundance to Estimate Summer Maize Planting Area

Multispectral satellite remote sensing data of low or moderate spatial resolution are widely used in large range crop planting area extraction.For those areas with complex structure，when the low or moderate spatial resolution remote sensing data sources is used to extract the planting area of target crop，mixed pixel is the main obstacle factor to restrict the area extracting precision.Extracting it on sub\|pixel scale could overcome the restriction of low or moderate spatial resolution and develop the extraction precision.However，the extraction method of target crop planting area on sub\|pixel scale now usually directly use the end\|member abundance to instead the percentage of planting area.Therefore it may cause some errors.On the basis of previous researches，taking Hebi City，Henan Province as the study area，which located in Huang\|Huai\|Hai plain，has the largest summer maize planting area and the complex planting structure.Taking FY3/MERSI data as the main information source.Using the method of spectral matched adaptive best end\|member combination of pixel unmixing to extract the summer maize end\|member abundances.Making regression modeling in various equation forms between summer maize end\|member abundances in pixel and the percentage of planting area.Then select the optimal regression equation form to build regression model，and estimate the actual summer maize ground planting area.Summing up the correlation coefficient when the model was building，significance test and the RMS errors condition of sample point verification.Then choose the cubic model to estimate the planting area of summer maize in the study area.It is proved by remote sensing estimation that the area precision of summer maize planting area is 97.1%，the position precision is 82.5%.     

Abundance estimation of crossover double particle swarms optimization for hyperspectral remote sensing imagery

ABSTRACT

Hyperspectral unmixing (HU) is an important technique for extracting materials and their abundance in hyperspectral remote sensing imagery. The presence of nonlinear mixing of light on the ground poses a difficult problem when estimating abundance fractions of all pixels. This problem makes the foundation of algorithms that can adapt all types of nonlinear mixing on the ground more complex and challenged. In this paper, a new bionic intelligent algorithm named crossover double particle swarms optimization (CDPSO) has been presented to estimate abundance for hyperspectral remote sensing imagery. The reconstruction error is used as the objective function for HU based on multilinear mixing model, and the nonlinear unmixing is transformed into an optimization problem. By improving the optimization performance of PSO for HU, we embed two types of new strategies, including double particle swarms crossover and swarm re-initialization, respectively. Our experiments, conducted using both synthetic and real hyperspectral data, demonstrate that the proposed CDPSO algorithm can outperform other state-of-the-art unmixing methods.     

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。     

凸体几何光谱解混研究进展及若干问题浅析

高光谱图像在高维特征空间中的凸体特性是凸体几何类光谱解混方法的理论依据，这类光谱解混方法具有直观性强、复杂度低、效率高等优点，是高光谱图像光谱解混方法研究的一个重要分支。本文旨在对国内外基于凸体几何理论的光谱解混方法进行回顾，指出这类方法研究中需要特别关注的若干问题，并着重对：①数据降维对凸体几何端元提取方法的影响，②两类经典的单形体体积衡量标准，③3个单形体体积计算公式及其关系3个问题进行简要分析，得到初步分析结果。