A comparative study of signal transformation techniques in automated spectral unmixing of infrared spectra for remote sensing applications

From geological and planetary exploration perspectives, automated sub-pixel classification of hyperspectral data is the most difficult task as it involves blind unmixing with library spectra of minerals. In this study, we demonstrate a procedure involving spectral transformation and linear unmixing to achieve the above task. For this purpose, infrared spectra of rocks from the spectral library, field, and remotely sensed hyperspectral image cube were used. Potential spectra of minerals for unmixing rock spectra were drawn from the library based on similarity of absorption features measured using Pearson correlation coefficient. Eight transformation techniques namely, first derivative, fast Fourier transform, discrete wavelet transform, Hilbert–Huang transform, crude low pass filter, S-transform, binary encoding, spectral effective peak matching, and two sparsity-based techniques (orthogonal matching pursuit, sparse unmixing via variable splitting, and augmented Lagrangian) were evaluated. Subsequently, minerals identified by above techniques were unmixed by linear mixture model (LMM) to decipher mineralogical composition and abundance. Results of LMM achieved using fully constrained least-square-estimation-based quadratic programming optimization approach were evaluated by conventional procedures such as X-ray diffraction and microscopy. In the case of image cube, endmembers derived using minimum noise fraction and pixel purity index were subjected to above procedure. It is evident that the discrete-wavelet-transformation-based approach produced excellent and meaningful results due to its flexibility in scaling the data and capability to handle noisy spectra. It is interesting to note that the adopted procedure could perform sub-pixel classification of image cube automatically and identify predominance of dolomite in limestone and sodium in alunite based on subtle differences in absorption positions.     

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。     

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.     

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。     

Identification and mapping of minerals in drill core using hyperspectral image analysis of infrared reflectance spectra

A Portable Infrared Mineral Analyzer II (PIMA II) field spectrometer was used to measure infrared reflectance spectra (1·3-2·5 μm) of split drill core at 1 cm intervals in both the along-core and cross-core directions. These data were formatted into an image cube similar to that acquired by an imaging spectrometer with 600 spectral channels, and multi-spectral and hyperspectral analysis techniques were used for analysis. Colour images and enhancements provided visual displays of the spectral information, while real-time digital extraction of individual spectra allowed identification of minerals. Absorption band-depth mapping and spectral classification were used to map the spatial distribution of specific minerals in the core. Linear spectral unmixing provided estimated mineral abundances. Analysis results demonstrate that multi-spectral and hyperspectral image analysis methods can be used to produce detailed mineralogical maps of drill core. They suggest that the concepts and analytical techniques developed for analysis of hyperspectral image data can be applied to field and laboratory spectra in a variety of disciplines, and raise the question of the use of hyperspectral scanners in the laboratory.     

高光谱图像非线性解混方法的研究进展

由于空间分辨率的限制,高光谱遥感图像中存在大量混合像元,对混合像元的解混是实现地物精确分类和识别的前提。与传统的线性解混方法相比,非线性解混方法在寻找组成混合像元的端元以及每个端元的丰度时具有较高的精度。分析了光谱非线性混合的原理,总结了近年来提出的非线性解混算法,重点对双线性模型、神经网络、基于核函数的非线性解混算法以及基于流形学习的非线性解混算法进行了介绍和分析。最后总结了混合像元非线性解混未来发展的趋势。     

Cropland distributions from temporal unmixing of MODIS data

Knowledge of the distribution of crop types is important for land management and trade decisions, and is needed to constrain remotely sensed estimates of variables, such as crop stress and productivity. The Moderate Resolution Imaging Spectroradiometer (MODIS) offers a unique combination of spectral, temporal, and spatial resolution compared to previous global sensors, making it a good candidate for large-scale crop type mapping. However, because of subpixel heterogeneity, the application of traditional hard classification approaches to MODIS data may result in significant errors in crop area estimation. We developed and tested a linear unmixing approach with MODIS that estimates subpixel fractions of crop area based on the temporal signature of reflectance throughout the growing season. In this method, termed probabilistic temporal unmixing (PTU), endmember sets were constructed using Landsat data to identify pure pixels, and uncertainty resulting from endmember variability was quantified using Monte Carlo simulation. This approach was evaluated using Landsat classification maps in two intensive agricultural regions, the Yaqui Valley (YV) of Mexico and the Southern Great Plains (SGP). Performance of the mixture model varied depending on the scale of comparison, with R² ranging from roughly 50% for estimating crop area within individual pixels to greater than 80% for crop cover within areas over 10 km². The results of this study demonstrate the importance of subpixel heterogeneity in cropland systems, and the potential of temporal unmixing to provide accurate and rapid assessments of land cover distributions using coarse resolution sensors, such as MODIS.     

Mapping microphytobenthos biomass by non-linear inversion of visible-infrared hyperspectral images

This study presents an innovative approach to map microphytobenthos biomass and fractional cover in Bourgneuf Bay (French Atlantic coast) using Digital Airborne Imaging Spectrometer (DAIS) hyperspectral data. Microphytobenthos is a microalgae forming a biofilm on the mudflat. Its spatial distribution is heterogeneous so it varies on a finer scale than that of airborne instrument spatial resolution, leading to a “mixed pixel” problem. Moreover, some microphytobenthic species form, at low tide, a biofilm deposited at the surface of the sediment substrate. The resulting signal is a highly non-linear combination of spectral endmembers due to microscale intimate mixtures. This prevents the use of classical linear unmixing models to retrieve biomass from reflectance spectra. A Modified Gaussian Model (MGM) is therefore used to remove the effects of surface roughness, shadowing and any other unknown processes that contribute to the overall shape (continuum) of the reflectance spectra. Then, relationships between microphytobenthos biomass and spectral shapes are derived from a spectral database compiled from laboratory reflectance spectra of microalgal monospecific cultures with different biomasses. Finally, microphytobenthos biomass and fractional cover are retrieved from the DAIS image by comparing the reflectance spectra of each pixel to a library of synthetic spectra corresponding to combinations of various biomasses and substrate percent cover. This new approach, when compared to more classically used ones such as indices, linear unmixing or spectral distance analysis, is proven to enable a much more reliable determination of biomass despite the large variety of substrates found in Bourgneuf Bay.     

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.     

Using classification and NDVI differencing methods for monitoring sparse vegetation coverage: a case study of saltcedar in Nevada,USA

A change detection experiment for an invasive species, saltcedar, near Lovelock, Nevada, was conducted with multidate Compact Airborne Spectrographic Imager (CASI) hyperspectral datasets. Classification and NDVI differencing change detection methods were tested. In the classification strategy, a principal component analysis (PCA) was performed on single‐date CASI imagery separately in the visible bands and NIR bands. Then the first five PCs from the visible bands and the first five PCs from the NIR bands were used to classify six to eight cover types with a maximum likelihood classifier. A complete matrix of change information and change/no‐change maps were produced by overlaying two single‐date classification maps. In the NDVI differencing strategy, a linear regression model was developed between two Normalized Difference Vegetation Index (NDVI) images to normalize the index differences caused by factors not related to land cover change. Then the actual time 2 NDVI image was subtracted by the predicted time 2 NDVI image to obtain the differencing image. The NDVI differencing image was further processed with a new threshold method into change/no‐change of saltcedar. By testing the single‐date classification results and validating the change/no‐change results, both change detection results indicated that CASI hyperspectral data have the potential to map and monitor the change of saltcedar. However, the accuracy assessment and change/no‐change validation results (overall accuracy 91.56% and kappa value 0.618 for the classification method against corresponding values of 93.04% and 0.684 for the NDVI differencing method) indicate that the NDVI differencing method outperformed the classification method in this particular study. In addition, use of the new method of determining thresholds for differentiating change pixels from no‐change pixels from the NDVI differencing image improved the change detection accuracy compared to a traditional method (kappa value increased from 0.813 to 0.888 from a test sample). Therefore, according to the criteria of higher accuracy of change/no‐change maps and fewer spectral bands, the NDVI differencing method is recommended for use if a suitable spectral normalization between multi‐temporal images can be carried out before performing image differencing.     

单形体体积最小化的差分进化光谱解混算法

目的 光谱解混是高光谱遥感图像处理的核心技术。当图像不满足纯像元假设条件时,传统算法难以适用,基于(单形体)体积最小化方法提供了一种有效的解决途径。然而这是一个复杂的约束最优化问题,更由于图像噪声等不确定性因素的存在,导致算法容易陷入局部解。方法 引入一种群智能优化技术-差分进化算法(DE),借助其较强的全局搜索能力以及优越的处理高维度问题的能力,并通过对问题编码,提出了一种体积最小化的差分进化(VolMin-DE)光谱解混算法。结果 模拟数据和真实数据实验的结果表明,与现有算法相比,该算法在15端元时精度(光谱角距离)可提高7.8%,当端元数目少于15个时,其精度普遍可以提高15%以上,特别是10端元时精度可以提高41.3%;在20~50 dB的噪声范围内,精度变化在1.9~3.2(单位:角度)之间,传统算法在2.2~3.5之间,表明该算法具有相对较好的噪声鲁棒性。结论 本文算法适用于具有纯像元以及不存在纯像元(建议最大纯度不低于0.8)这两种情况的高光谱遥感图像,并可在原始光谱维度进行光谱解混,从而避免降维所带来的累计误差,因此具有更好的适应范围和应用前景。     

Spectral unmixing of Landsat Thematic Mapper data

The observed spectral signature of pixels in remote sensing imagery in most cases is the result of the reflecting properties of a number of surface materials constituting the area of a pixel. Despite this knowledge most image classification techniques aim at labelling a pixel according to a singular surface category. An alternative product can be generated using spectral unmixing: a technique that strives to find the surface abundances of a number of spectral components together causing the observed spectral reflectance at a pixel. A stepwise approach to implement spectral unmixing in Landsat Thematic Mapper image analysis is proposed: (1) atmospheric calibration of the image data, (2) preselection of a large number of ‘candidate’ endmembers, (3) reduction to the most important spectral endmembers using spectral angle mapping, (4) finding the relative abundances of the endmembers through spectral unmixing analysis, (5) combining the abundance estimates into a final product comparable to a classified image, and (6) accuracy assessment. A Landsat Thematic Mapper image from southern Spain covering a large peridotite body with adjacent limestone and low-grade metamorphic rocks is used as an example to demonstrate the usefulness of unmixing.     

Subpixel monitoring of the seasonal snow cover with MODIS at 250 m spatial resolution in the Southern Alps of New Zealand: Methodology and accuracy assessment

This study describes a comprehensive method to produce routinely regional maps of seasonal snow cover in the Southern Alps of New Zealand (upper Waitaki basin) on a subpixel basis, and with the MODerate Resolution Imaging Spectroradiometer (MODIS). The method uses an image fusion algorithm to produce snow maps at an improved 250 m spatial resolution in addition to the 500 m resolution snow maps. An iterative approach is used to correct imagery for both atmospheric and topographic effects using daily observations of atmospheric parameters. The computation of ground spectral reflectance enabled the use of image-independent end-members in a constrained linear unmixing technique to achieve a robust estimation of subpixel snow fractions. The accuracy of the snow maps and performance of the algorithm were assessed carefully using eight pairs of synchronic MODIS/ASTER images. ‘Pixel-based’ metrics showed that subpixel snow fractions were retrieved with a Mean Absolute Error of 6.8% at 250 m spatial resolution and 5.1% after aggregation at 500 m spatial resolution. In addition, a ‘feature-based’ metric showed that 90% of the snowlines were depicted generally within 300 m and 200 m of their correct position for the 500-m and 250-m spatial resolution snow maps, respectively. A dataset of 679 maps of subpixel snow fraction was produced for the period from February 2000 to May 2007. These repeated observations of the seasonal snow cover will benefit the ongoing effort to model snowmelt runoff in the region and to improve the estimation and management of water resources.     

Detection and classification of invasive saltcedar through high spatial resolution airborne hyperspectral imagery

We evaluated the performance of airborne HyperSpecTIR (HST) images for detecting and classifying the invasive riparian vegetation saltcedar along the Muddy River in Clark County, Nevada. HyperSpecTIR image reflectance spectra (227 bands, 450–2450 nm) were acquired for the following four vegetation covers: invasive saltcedar, native honey mesquite, grassland patches and crops. We compared five feature reduction approaches: band selection based on Jeffreys–Matusita distance, principal component analysis (PCA), minimum noise fraction (MNF), segmented principal component transform (SPCT) and segmented minimum noise fraction (SMNF). In addition, maximum likelihood (ML) and two spectral angle mapper (SAM) classifiers were applied to all extracted bands or features. Classification accuracies were compared among all classification approaches. Although the overall accuracy of maximal likelihood classifiers generally surpassed that of SAM classifiers, the highest overall accuracy was achieved by a SMNF-SAM combination with adjusted angular thresholds for classes. We concluded that high spectral and spatial resolution imagery can be used to detect and classify invasive saltcedar in this arid area.     

谐波能量谱特征向量的高光谱影像Bayes分类

摘要：对于高光谱影像存在高维非线性、数据冗余多、纯训练样本难以提取等不足,本文引入频率域空间的谐波分析（Harmonic Analysis,HA）理论并提出了一种高光谱影像的HA-Bayes监督分类方法。该方法在保持高光谱数据空-谱特性不变的情况下,从光谱维角度分析不同分解层的影像光谱谐波特征,将高光谱影像变换成由谐波能量谱组成的频率域特征矢量信息。通过建立谐波能量谱特征向量的先验知识,实现Bayes准则下谐波能量谱特征矢量信息判别与分类,最终实现高光谱影像分类。将此方法应用到ROSIS高光谱影像分类时获得的分类总体精度达85.5%,Kappa系数也达到了0.812。进一步实验也证明频率域的谐波分析在高光谱遥感影像特征提取与分类方面具有更好的优势和潜力。     

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。     

The effect of spectral and spatial degradation of hyperspectral imagery for the Sclerophyll tree species classification

Hyperspectral imaging can be a useful remote-sensing technology for classifying tree species. Prior to the image classification stage, effective mapping endeavours must first identify the optimal spectral and spatial resolutions for discriminating the species of interest. Such a procedure may contribute to improving the classification accuracy, as well as the image acquisition planning. In this work, we address the effect of degrading the original bandwidth and pixel size of a hyperspectral and hyperspatial image for the classification of Sclerophyll forest tree species. A HySpex-VNIR 1600 airborne-based hyperspectral image with submetric spatial resolution was acquired in December 2009 for a native forest located in the foothills of the Andes of central Chile. The main tree species of this forest were then sampled in the field between January and February 2010. The original image spectral and spatial resolutions (160 bands with a width of 3.7 nm and pixel sizes of 0.3 m) were systematically degraded by resampling using a Gaussian model and a nearest neighbour method, respectively (until reaching 39 bands with a width of 14.8 nm and pixel sizes of 2.4 m). As a result, 12 images with different spectral and spatial resolution combinations were created. Subsequently, these images were noise-reduced using the minimum noise fraction procedure and 12 additional images were created. Statistical class separabilities from the spectral divergence measure and an assessment of classification accuracy of two supervised hyperspectral classifiers (spectral angle mapper (SAM) and spectral information divergence (SID)) were applied for each of the 24 images. The best overall and per-class classification accuracies (>80%) were observed when the SAM classifier was applied on the noise-reduced reflectance image at its original spectral and spatial resolutions. This result indicates that pixels somewhat smaller than the tree canopy diameters were the most appropriate to represent the spatial variability of the tree species of interest. On the other hand, it suggests that noise-reduced bands derived from the full image spectral resolution rendered the best discrimination of the spectral properties of the tree species of interest. Meanwhile, the better performance of SAM over SID may result from the ability of the former to classify tree species regardless of the illumination differences in the image. This technical approach can be particularly useful in native forest environments, where the irregular surface of the uppermost canopy is subject to a differentiated illumination.     

Broadleaf species recognition with in situ hyperspectral data

Timely and accurate identification of tree species by spectral methods is crucial for forest and urban ecological management. In this study, a total of 394 reflectance spectra (between 350 and 2500 nm) from foliage branches or canopy of 11 important urban forest broadleaf species were measured in the City of Tampa, Florida, USA with a spectrometer. The 11 species include American elm (Ulmus americana), bluejack oak (Quercus incana), crape myrtle (Lagerstroemia indica), laurel oak (Q. laurifolia), live oak (Q. virginiana), southern magnolia (Magnolia grandiflora), persimmon (Diospyros virginiana), red maple (Acer rubrum), sand live oak (Q. geminata), American sycamore (Platanus occidentalis), and turkey oak (Q. laevis). A total of 46 spectral variables, including normalized spectra, derivative spectra, spectral vegetation indices, spectral position variables, and spectral absorption features were extracted and analysed from the in situ hyperspectral measurements. Two classification algorithms were used to identify the 11 broadleaf species: a nonlinear artificial neural network (ANN) and a linear discriminant analysis (LDA). An analysis of variance (ANOVA) indicates that the 30 selected spectral variables are effective to differentiate the 11 species. The 30 selected spectral variables account for water absorption features at 970, 1200, and 1750 nm and reflect characteristics of pigments and other biochemicals in tree leaves, especially variability of chlorophyll content in leaves. The experimental results indicate that both classification algorithms (ANN and LDA) have produced acceptable accuracies (overall accuracy from 86.3% to 87.8%, kappa from 0.83 to 0.87) and have a similar performance for classifying the 11 broadleaf species with input of the 30 selected spectral variables. The preliminary results of identifying the 11 species with the in situ hyperspectral data imply that with current remote sensing techniques, including high spatial and spectral resolution data, it is still difficult but possible to identify similar species to such 11 broadleaf species with an acceptable accuracy.     

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.     

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。