WorldView-2影像的湿地典型挺水植物群落含水量估算研究——以北京野鸭湖湿地为例

利用多光谱遥感技术定量估算野鸭湖湿地挺水植物的含水量.基于典型挺水植物的实测冠层光谱及其对应样方的叶片含水量和叶面积指数LAI数据,首先对芦苇和香蒲的地面实测光谱进行重采样,以模拟WorldView-2影像的光谱,然后利用模拟光谱分别构建芦苇和香蒲任意两波段反射率组合而成的比值(SR)和归一化差值植被指数(NDVI),通过分析植被指数与CWC(冠层含水量,Canopy Water Content)的相关关系,选择与CWC显著相关的植被指数,并通过单变量线性与非线性拟合的分析方法确定监测不同挺水植物群落的最佳植被指数,建立估算模型;结合覆盖研究区的WorldView-2高分辨率多光谱影像,对研究区的挺水植物群落CWC进行反演及制图.结果表明,基于模拟WorldView-2影像光谱构建的比值(SR)和归一化差值植被指数(NDVI)与CWC的总体相关性较高;SR(8,3)芦苇为估算CWC芦苇的最优植被指数,估算模型为y=0.005x+0.003,NDVI(8,3)香蒲为估算CWC香蒲的最优植被指数,估算模型为y=2.461x2-0.313x+0.032,通过交叉检验,CWC芦苇和CWC香蒲的预测精度分别为87.42%和82.12%,预测精度较为理想;利用实测数据对反演的CWC空间分布图进行了验证,通过验证,芦苇和香蒲影像估算CWC的均方根差(RMSE)分别为0.0048和0.0052,估算精度分别为83.56%和80.31%,表明利用WorldView-2高分辨率多光谱影像反演湿地挺水植物群落CWC具有较高的可行性.     

直接正交校正用于牛奶成分近红外光谱分析

介绍采用近红外光谱分析方法快速检测牛奶主要成分含量的测量原理,探讨研究直接正交(DO)校正的基本方法.利用牛奶成分近红外光谱测量系统分别采集牛奶样品和葡萄糖白蛋白两成分溶液样品的近红外光谱,采用DO法进行光谱数据预处理,并采用偏最小二乘(PLS)法分别建立其相应的数学模型.实验及数据处理结果表明:经DO法预处理后,滤除了原始光谱中的部分噪声信息,但保留了原始光谱中的主要信息.PLS校正模型采纳的最佳因子数随着DO因子的依次滤除相应减少.牛奶中脂肪和蛋白质校正模型在原始光谱分别被滤除3和4个主成分时达到性能最佳,校正标准偏差SEC分别为0.3204和0.2727,预测标准偏差SEP为0.7316和0.4460,两成分溶液样品中白蛋白和葡萄糖校正模型在原始光谱被滤除1个因子时达到性能最佳.校正标准偏差SEC分别为0.2513和0.2780,预测标准偏差SEP为0.5169和0.7870,单位(g/dL),与DO法预处理之前的PLS模型相比,预测标准偏差相应降低,采纳的主成分数减少,模型得到简化.     

近海污染大气气溶胶光学性质反演研究

提出了基于中分辨率成像光谱仪(MODIS)数据反演近海污染大气气溶胶光学性质的算法.根据近海污染大气气溶胶的成分特征构建了新的气溶胶模式;同时避开了MODIS反演算法中计算大气顶光谱反射率理论的缺陷;利用近红外光谱数据降低了近岸二类水体离水辐射的影响,并通过光谱匹配技术实现了近海污染大气气溶胶光学性质的反演.利用气溶胶...     

高光谱微波亮温数据预处理方法分析

高光谱亮温数据的数据量通常十分庞大,其中包含大量冗余信息,直接使用计算量大且冗余信息会对计算结果产生影响,所以需要对数据进行预处理。使用主成分分析法和通道选择方法2种数据预处理方法对根据SeeborV5.0数据集生成的高光谱仿真微波亮温数据进行预处理,将处理后的数据作为神经网络的输入进行大气温度廓线反演。反演实验结果表明,使用主成分分析法的神经网络反演效果更好。     

森林冠层氮含量遥感估算

使用高光谱数据估算叶片与冠层尺度的森林氮含量.首先采用基于高斯误差函数的BP神经网络Erf-BP建立叶片尺度氮含量的遥感估算模型;其次根据几何光学模型原理,推导冠层光谱与叶片光谱的尺度转化函数,将Hy-perion影像的冠层光谱转换到叶片尺度并反演叶片尺度的氮含量;最后,利用森林结构参数LAI得到研究区域冠层尺度氮含量.结果表明,隐含层包含8个神经元的Erf-BP模型最优,检验精度为76.8597%;利用尺度转化函数估算670 nm和865 nm冠层光谱与实测光谱决定系数为0.5203和0.4117;反演叶片尺度氮含量与实测数据的决定系数为0.7019;该方法为高精度快速估算叶片和冠层尺度森林氮含量提供参考.     

基于可见光/近红外光谱技术的倒伏水稻识别研究

运用可见光/近红外光谱仪获取正常的和受稻飞虱、穗颈瘟危害而倒伏的水稻冠层光谱反射率,采用主成分分析(PCA)方法对反射率光谱进行降维处理,提取2个主分量光谱.其中,第一主分量PC1代表了水稻冠层的光谱特性,第二主分量PC2反映了倒伏水稻的冠层光谱变化信息.将前2个主分量作为支持向量分类机(SVC)的输入向量,建立分类模型.结果表明,对受稻飞虱危害倒伏的水稻验证数据的识别精度为100%,对受穗颈瘟危害倒伏的水稻验证数据的识别精度为90.9%.研究表明可见光/近红外光谱可能是一种有效的倒伏水稻识别方法.     

拉曼光谱结合Adaboost算法的食源性致病菌分类识别

食源性致病菌的检测分类一直是食品安全领域的重要研究对象,与传统的病原菌分类方法相比,基于拉曼光谱的分类识别方法具有更高的灵活性和准确性.实验以常见食源性致病菌的拉曼光谱为对象,利用11种病原菌的132条光谱数据,提出一种基于主成成分分析(PCA)和线性判别分析(LDA)的Adaboost集成分类识别模型.实验结果表明,...     

基于超像素分割与卷积神经网络的高光谱图像分类

针对卷积神经网络（CNN）在分类高光谱图像时空-谱特征利用率不足和分类效率低的问题，提出基于超像素分割与CNN的高光谱图像分类方法。首先利用主成分分析（PCA）提取图像的前12个成分后对前3个主成分进行滤波，对滤波后的3个波段进行超像素分割；然后将样本点映射到超像素内，使其以超像素而不是像素为基本的分类单元；最后利用CNN进行图像分割。在两个公共的数据集WHU-Hi-Longkou和WHU-Hi-HongHu上进行实验，实验结果表明，相比仅利用光谱信息的方法，融合空-谱特征信息的方法的精度得到提升，在两个数据集上的分类精度分别达99.45%和97.60%。     

空谱超像素核极限学习机的高光谱分类算法

针对高光谱图像中光谱信息提取时高维特征向量由于部分邻域叠加造成数据缺损,以及图像局部区域像素点在空间结构信息中存在同谱异类现象和密度差异的问题,提出了一种基于空谱超像素融合核极限学习机(SSKELM)的高光谱图像分类算法.对光谱空间第一主成分分量进行超像素分割,每个超像素被看作一个形状自适应区域。利用空间信息、超像素内...     

基于氧气A 带的高光谱卫星气溶胶层高优化反演

针对气溶胶被动卫星遥感中由于气溶胶模型的不确定性导致的反演误差, 引入了一种基于贝叶斯理论的新型 气溶胶层高反演算法, 并应用于哨兵5 先导(Sentinel-5P) 卫星的TROPOMI (TROPOspheric Monitoring Instrument) 载 荷。该算法基于不同候选气溶胶模型的模型证据(气溶胶模型的条件概率密度) 确定符合当前观测数据条件的气溶胶 模型, 并通过两种模型选择方案分别得到估算最大值解和估算平均值解作为反演结果。以TROPOMI 观测到的一次真 实野火事件为例, 反演结果和官方产品具有很好的空间一致性, 且明显降低了低估现象, 证明在气溶胶先验知识缺乏 的背景下该算法能够高效选择合适的气溶胶模型, 为今后高光谱卫星气溶胶层高反演的业务化数据处理提供了一种 新的解决方案。     

Bayesian approach with hidden Markov modeling and mean field approximation for hyperspectral data analysis

The main problems in hyperspectral image analysis are spectral classification, segmentation, and data reduction. In this paper, we propose a Bayesian estimation approach which gives a joint solution for these problems. The problem is modeled as a blind sources separation (BSS). The data are M hyperspectral images and the sources are K < M images which are composed of compact homogeneous regions and have mutually disjoint supports. The set of all these regions cover the total surface of the observed scene. To insure these properties, we propose a hierarchical Markov model for the sources with a common hidden classification field which is modeled via a Potts-Markov field. The joint Bayesian estimation of the hidden variable, sources, and the mixing matrix of the BSS gives a solution for all three problems: spectra classification, segmentation, and data reduction of hyperspectral images. The mean field approximation (MFA) algorithm for the posterior laws is proposed for the effective Bayesian computation. Finally, some results of the application of the proposed methods on simulated and real data are given to illustrate the performance of the proposed method compared to other classical methods, such as PCA and ICA.     

基于本征模函数的高光谱数据特征提取方法

针对基于参数估计的特征提取方法高光谱数据维数高参数估计偏差大、细节光谱信息易丢失等问题，引入经验模式分解理论，提出了基于本征模函数的高光谱数据特征提取方法。该方法通过计算光谱特征的最大最小值以及均值得到本征模函数，从而得到反映高光谱数据的不同尺度的光谱波形波动信息，即吸收特征信息，并将高光谱数据投影到本征模函数空间，从而实现高光谱数据中不同物质属性光谱特征提取。利用航空推扫式成像光谱仪数据进行方法性能分析与验证，试验结果表明该方法不需要进行统计参数估计，避免了高光谱数据协方差的奇异性和参数估计不准确的影响，并较好地保留了数据提供的所有信息，增大了数据类间可分性。     

Multivariate Gaussian MRF for multispectral scene segmentation andanomaly detection

Gaussian Markov random field texture models and multivariate parametric clustering algorithms have been applied extensively for segmentation, restoration, and anomaly detection of single-band and multispectral imagery, respectively. The present work extends and combines these previous efforts to demonstrate joint spatial-spectral modeling of multispectral imagery, a multivariate (vector observations) GMRF texture model is employed. Algorithms for parameter estimation and image segmentation are discussed, and a new anomaly detection technique is developed. The model is applied to imagery from the Daedalus sensor. Image segmentation results from test images are discussed and compared to spectral clustering results. The test images are collages, with known texture boundaries constructed from larger data cubes. Anomaly detection results for two Daedalus images are also presented, assessed using receiver operating characteristic (ROC) performance curves, and compared to spectral clustering models. It is demonstrated that even the simplest first-order isotropic texture models provide significant improvement in image segmentation and anomaly detection over pure spectral clustering for the data sets examined. The sensitivity of anomaly detection performance to the choice of parameter estimation method and to the number of texture segments is examined for one example data set     

基于AMSR-2数据的多种射频干扰识别方法的比较

由于射频干扰（radio-frequency interference，RFI）对星载微波遥感的影响日益严重，为了比较各种识别方法的适用性，基于先进微波扫描辐射计（advanced microwave scanning radiometer 2，AMSR-2）2015年冬季和夏季的观测亮度温度，分别采用谱差法、多通道回归分析法、双主成分分析法和比值法，对欧洲陆地区域和海洋区域的10.65 GHz和7.3 GHz频率干扰进行识别和分析，并对比几种方法的月综合结果.结果表明，冬季时谱差法在陆地上受积雪散射的影响，会将积雪下垫面误判为RFI污染；多通道回归分析法和双主成分分析法识别出的结果类似，无论陆地和洋面还是冬季和夏季均可有效使用；当10.65 GHz和7.3 GHz波段观测同时存在RFI时，比值法将不能识别出10.65 GHz的RFI信号，并且比值法会将海陆边界及附近视场的观测误判为RFI信号.多通道回归分析法和双主成分分析法的适用范围较为广泛，而谱差法和比值法各有弊端.     

Iterative transductive learning for automatic image segmentation and matting with RGB-D data

In this paper, we propose a fully automatic image segmentation and matting approach with RGB-Depth (RGB-D) data based on iterative transductive learning. The algorithm consists of two key elements: robust hard segmentation for trimap generation, and iterative transductive learning based image matting. The hard segmentation step is formulated as a Maximum A Posterior (MAP) estimation problem, where we iteratively perform depth refinement and bi-layer classification to achieve optimal results. For image matting, we propose a transductive learning algorithm that iteratively adjusts the weights between the objective function and the constraints, overcoming common issues such as over-smoothness in existing methods. In addition, we present a new way to form the Laplacian matrix in transductive learning by ranking similarities of neighboring pixels, which is essential to efficient and accurate matting. Extensive experimental results are reported to demonstrate the state-of-the-art performance of our method both subjectively and quantitatively.     

Postflood damage evaluation using Landsat TM and ETM+ data integrated with DEM

In recent decades, radar and optical satellite imagery have been used for evaluating flooding extent. In this paper, a straightforward technique based on the sequential use of the spectral-temporal principal component analysis, logical filtering, and image segmentation integrated with the digital elevation model was developed as a decisional support tool for the allocations of the resource destined for the flooded areas. The mapping technique was first applied to the catastrophic event that occurred in the Piemonte Region (Italy) in November 1994, which was the worst event of the past century for that region, with 44 casualities and over 2000 homeless. Next, it was applied to the Obion/Forked Deer inundation that occurred in Tennessee (U.S.) between November and December 2001, in which heavy damage to the infrastructure was reported. Two Landsat-5 Thematic Mapper (path 194, row 28/29) and two Landsat-7 Enhanced Thematic Mapper Plus (path 23, row 35) images were processed, two of them collected before and two after the events. The method proposed proved to be an effective approach for evaluating flood extent and for assessing the damage produced by the flooding. An overall accuracy of 85.6%, a user accuracy of 87.5%, and a producer accuracy of 97.5% were achieved, and an agreement of 83% between ground measures and remotely sensed data in the estimation of flood water volumes was also achieved on a regional scale.     

头发光谱显微成像数据处理分析方法及其应用

随着多通道探测技术、集成显微技术和计算机及分析技术的发展,红外和拉曼光谱技术及光谱显微成像技术在生物及医学领域得到广泛应用并成为一种向实用化发展的趋势。但是,要从光谱测量中获得准确、有用的信息需要对光谱及成像数据进行细致、合理的分析。本文工作应用MATLAB软件编程,结合单谱线分析和多谱线多变量分析的方法,对健康人和病人头发横截面微观扫描的拉曼光谱数据进行分析,结果表明,通过对头发拉曼光谱细致处理和数据分析可以揭示被检测人之间的差异。这项工作是把拉曼光谱成像技术及相关光谱分析方法应用于生物医学领域的一个实例。     

光纤光谱结合模式识别无损检测苹果表面疤痕

为了实现基于光纤光谱技术结合模式识别无损检测苹果表面疤痕, 利用光纤光谱采集系统采集了完好无损和表面有疤痕苹果的光谱数据, 采用标准正态变换(SNV)和1阶导数对原始光谱数据进行预处理; 利用主成分分析方法对预处理后的光谱数据进行降维, 以提取能反映苹果表面疤痕的特征光谱; 利用k最近邻(KNN)模式识别方法和偏最小二乘判别分析方法, 建立了苹果表面疤痕的识别模型。结果表明, 采用主成分分析法选择了累计贡献率超过99%的前8个主成分作为样本集特征光谱数据, 很好地实现了光谱数据的降维; 利用1阶导数+KNN识别模型对校正集以及SNV+KNN识别模型对预测集中正常果和疤痕果的正确率识别均高达96.0%。验证了基于光纤光谱技术结合模式识别方法无损检测苹果表面疤痕的可行性。     

好奇号火星车ChemCam-LIBS 光谱数据的定量分析研究

传统的偏最小二乘法和支持向量机回归等方法在预测光谱对应的火星车地面标样成分元素含量时往往难以获得较高的精度,并难于进一步优化。针对上述问题,在研究中对高维度光谱信息进行三通道折叠以消除其基体效应,并引入在计算机视觉领域表现不俗的ResNet残差网络结构来提取光谱特征并预测对应主成分含量值。文中将ResNet网络结构中的全连接层去除以避免模型参数快速增长,并将网络最后的Softmax分类子层改为线性整流层以便于进行预测,同时添加了指数学习率衰减和Dropout机制以使模型预测结果具备更高的精度与泛化能力。模型各主要元素含量的预测均方根误差相对于线性支持向量机LinearSVR和深度可分离卷积网络Xception分别平均下降了30%和17%。实验结果表明:采用LIBS技术对ChemCam光谱数据进行主成分元素定量分析时,基于ResNet网络建立的回归模型表现出良好的预测特性。     

Embedding topic discovery in conditional random fields model for segmenting nuclei using multispectral data

Segmentation of cells/nuclei is a challenging problem in 2-D histological and cytological images. Although a large number of algorithms have been proposed, newer efforts continue to be devoted to investigate robust models that could have high level of adaptability with regard to considerable amount of image variability. In this paper, we propose a multiclassification conditional random fields (CRFs) model using a combination of low-level cues (bottom-up) and high-level contextual information (top-down) for separating nuclei from the background. In our approach, the contextual information is extracted by an unsupervised topic discovery process, which efficiently helps to suppress segmentation errors caused by intensity inhomogeneity and variable chromatin texture. In addition, we propose a multilayer CRF, an extension of the traditional single-layer CRF, to handle high-dimensional dataset obtained through spectral microscopy, which provides combined benefits of spectroscopy and imaging microscopy, resulting in the ability to acquire spectral images of microscopic specimen. The approach is evaluated with color images, as well as spectral images. The overall accuracy of the proposed segmentation algorithm reaches 95% when applying multilayer CRF model to the spectral microscopy dataset. Experiments also show that our method outperforms seeded watershed, a widely used algorithm for cell segmentation.