基于区域活动轮廓模型的高光谱图像分割方法

针对高光谱图像特点,提出了一种基于区域活动轮廓模型的高光谱图像分割方法。综合考虑高光谱图像的空间信息和光谱信息,对Chan\|Vese方法中的能量函数加以改进,利用空间全局信息和同质区域的灰度一致性,约束能量函数空间项;利用目标光谱信息相似性,约束能量函数光谱项,最后通过能量函数最小化实现图像分割。该方法能够有效提取高光谱图像中的模糊轮廓,从而降低混合像元和目标周围阴影对分割造成的影响。利用两幅AVIRIS图像进行仿真实验,实验结果表明,提出的方法能够获得令人满意的分割效果,并且对复杂场景具有一定适应性。     

高光谱图像在生物医学中的应用

高光谱成像（hyperspectral imaging,HSI）作为生物医学可视化的一种新兴技术,在生物医学领域的研究正逐渐受到关注。随着高光谱成像技术以及精准医学的迅速发展,将高光谱成像技术应用于近距离的医学诊断成为新的研究趋势。高光谱成像技术能同时获取生物组织的2维空间信息和1维光谱信息,覆盖可见光、红外和紫外等光谱范围,具有较高的光谱分辨率,可提供有关组织生理、形态和生化成分的诊断信息,为生物组织学研究提供更精细的光谱特征,进而为医学病理诊断提供更多辅助信息。本文介绍了高光谱成像技术的基本原理、高光谱显微成像系统的基本构成及特点。基于此,总结并阐述了高光谱成像技术在疾病诊断和手术指导中的应用进展,涉及其在癌症、心脏病、视网膜疾病、糖尿病足、休克、组织病理学和图像引导手术等方面的应用。综合分析了高光谱成像技术在生物医学领域应用的局限性,并提出了生物医学研究领域中该技术的未来发展方向。     

高光谱遥感技术的铅污染监测应用研究

从高光谱技术的发展及其在植被遥感领域的应用出发，探讨了植被高光谱特征研究及其常用分析方法，对在营养胁迫情况下植物的叶绿素等变化对高光谱遥感响应的特征进行分析；归纳了获得优势应用的植被指数和导数光谱，植被光谱信息提取方法；总结了土壤遭受重金属铅污染及其对植物的影响的机理；针对遭受污染胁迫的植物的地面高光谱特征提出了利用地面高光谱遥感技术作为土壤及植物重金属污染的遥感监测手段；在探讨其应用现状的同时展望了高光谱遥感数据支撑与校验的应用前景。     

基于分类KLT的高光谱图像压缩

高光谱图像的有效压缩已经成为高光谱遥感领域研究的热点。提出了一种基于分类KLT（Karhunen-Loève Transform）的高光谱图像压缩算法。该算法利用光谱信息对高光谱图像进行地物分类,根据相邻波段的相关性对高光谱图像进行波段分组。在地物分类与波段分组的基础上,对每组的每一类地物数据分别进行KL变换,利用EBCOT（Embedded Block Coding with Optimal Truncation）算法对所有主成分进行联合编码。实验结果表明,该算法能够取得优于JPEG2000以及DWT-JPEG2000的压缩性能,适合实现高光谱图像的有效压缩。     

基于分类KLT的高光谱图像压缩

高光谱图像的有效压缩已经成为高光谱遥感领域研究的热点。提出了一种基于分类KLT（ Karhunen-Loeve Transform）的高光谱图像压缩算法。该算法利用光谱信息对高光谱图像进行地物分类,根据相邻波段的相关性对高光谱图像进行波段分组。在地物分类与波段分组的基础上,对每组的每一类地物数据分别进行KL变换,利用EBCOT（Embedded Block Coding with Optimal Trtmcation）算法对所有主成分进行联合编码。实验结果表明,该算法能够取得优于JPEG2000以及DWT-JPEG2000的压缩性能,适合实现高光谱图像的有效压缩。     

基于子空间-粗集法的高光谱数据光谱与纹理特征优选

为提高光谱数据光谱信息和纹理信息利用率,提出基于自动子空间划分和粗集理论的光谱与纹理特征优选方法。该方法在传统子空间划分法的基础上,利用粗集约简思想对不同类别地物光谱特征进行约简,得到基于光谱的初选波段,再利用灰度共生矩阵法计算出初选光谱波段的纹理信息,并约简优选,得到基于光谱和纹理信息的终选波段。利用黑河生态水文遥感试验中所获取的机载高光谱数据CASI,开展该方法的实证研究。对原始光谱波段、初选光谱波段和终选波段进行SVM(Support Vector Machine)分类,结果表明:与原始光谱数据相比,经过光谱初选得到的初选波段和增加纹理优选的终选波段,总体分类精度分别提高了0.84%和2.78%,Kappa系数分别提高了0.01和0.035;对地物纹理信息进行深度挖掘可以进一步提高遥感影像分类精度。     

基于分形信号的高光谱影像增强方法

目前常用的高光谱影像增强方法大多继承了多光谱影像的增强处理方法,这类方法没有充分利用光谱信息,而基于混合像元分解的图像增强方法存在端元的选取问题。基于影像的自相似特征,探索运用分形信号进行遥感影像增强的可能性。以3景Hyperion高光谱影像数据为基础,把基于地毯的方法进行修正后用于计算高光谱影像中每一像元的分形信号。结果表明,与原始高光谱影像相比,分形信号影像可以更好地突出地物特征,从而达到影像增强的目的,原始曲线形态特征、初始尺度的选择以及采样点数目对分形信号和分形特征尺度均有影响。     

基于全卷积网络和自编码的高光谱图像分类

针对高光谱图像空间信息利用不足、标记样本数量较少的问题,提出一种基于全卷积网络和堆栈稀疏自编码的高光谱图像分类算法。基于迁移学习的思想,利用预训练好的全卷积网络FCN-8s,挖掘图像潜在的多尺度几何结构特征;选取其特征的像素邻域信息,采用拼接融合的方法与原光谱信息进行融合;利用堆栈稀疏自编码网络完成最终的多尺度空谱特征提取,并通过Softmax分类器实现分类。对三组遥感图像进行实验,结果显示,所提算法极大改善了边界区域的分类效果。     

基于独立空谱残差融合的联合稀疏表示高光谱图像分类

针对高光谱图像分类中存在的空间信息与光谱信息融合问题,提出一种基于独立空谱残差融合的联合稀疏表示高光谱图像分类算法。使用类独立的光谱角初分类图像,获得像元初始标记后按特定条件进行筛选再构造像元邻域空间。提取图像的全局空间信息,并将其引入到空谱联合稀疏表示模型中,使用单独的光谱信息字典与空间信息字典分别进行图像光谱与空间的联合稀疏表示残差计算。在此基础上,使用残差融合算法确定图像类别。实验结果表明,相对SVM、KNN等算法,该算法能够提升高光谱图像的分类精度,且分类结果更稳定。     

基于类别可分性的高光谱图像波段选择

高光谱遥感数据具有丰富的光谱信息,应用十分广泛,但其冗余的光谱信息有时会限制高光谱图像的分类等的精度以及计算复杂度.为了提高解译效率,高光谱图像降维不可或缺,这也是高光谱图像处理的研究热点之一.提出了一种基于类别可分性的高光谱图像波段选择方法(Endmember Separability Based band Selection,ESBB),该方法通过Mahalanobis距离最大化图像中各类地物的可分性来确定最优的波段组合.相较于其他监督波段选择算法,该方法不需要大量训练样本,不用对每个组合做分类处理.对波段选择后的结果进行分类的实验结果证明,该方法是一个快速有效的波段选择方法,可以得到一个较好的分类精度.     

基于萤火虫算法的高光谱遥感波段选择方法

高光谱图像在遥感领域中的应用越来越广泛,但由于自身的高数据维、波段间的高冗余度等特性给图像处理带来了一定困难,针对这个问题,提出一种基于类间可分性准则的改进萤火虫仿生算法,进行高光谱遥感波段选择。在分析萤火虫算法机理的基础上,阐述了利用该算法进行高光谱波段选择的思路,并构造波段相似性矩阵,选择欧氏距离、JM距离、光谱信息散度和离散度作为可分性准则来设置目标函数,根据目标函数值的优劣选择优势波段。最后,使用HYDICE Washington DC Mall和 HyMap Purdue Campus两个高光谱遥感影像数据进行实验验证,并利用支持向量机分类器对最佳波段组合进行精度评价,证明该算法的可行性和有效性。
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高光谱影像的BDT-SVM地物分类算法与应用

面对海量数据的特征空间高维性及训练样本的有限性,高光谱遥感影像若采用常规统计模式的分类方法难以获得较好的分类结果。因此探讨支持向量机(SVM)分类器的基本原理,针对EO-1Hyperion高光谱影像的分类特点及现有多类SVM算法所存在的训练时间长及分类精度低等问题,引入二叉决策树SVM(BDT-SVM)分类算法,并提出一种新的类间分离度定义方法及相应的客观确定二叉树结构的策略,由此生成改进的BDT-SVM算法。实验结果表明:与其他多类分类方法相比,基于改进的BDT-SVM算法的高光谱影像地物分类效果更好,总体精度达到90.96%,Kappa系数为0.89,该算法还解决了经典SVM多类分类可能存在的不可分区域问题。     

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.     

成像光谱数据挖掘与矿物填图技术研究

评述了当前成像光谱测量技术的发展和应用现状。以地面高光谱测量、航空成像光谱测量、航天高光谱图像处理分析及室内数据模拟等多维立体研究方式,采用多源空间数据挖掘、弱信息提取、光谱建模及分类识别等关键技术,结合岩石矿物光谱吸收特征形成机理分析,开展成像光谱矿物填图技术的研究。根据实际的成像光谱数据特点与应用需求,提出成像光谱数据挖掘与矿物填图技术应用流程。     

An improved classification method for hyperspectral data based on spectral and morphological information

Combining spectral and morphological information to classify hyperspectral data offers a considerable advantage over methods based solely on spectral or morphological data. Previously, a classification method was proposed that concatenated extended morphological profile (EMP) and spectral information into one feature vector for each pixel of an image. Although this method has the merit of simultaneously using spectral and morphological information, it runs the risk of generating new spectral constituents (not present in the original image). In this letter, an improved classification method based on fusing extended multivariate morphological profile (EMMP) and spectral information was proposed. A new vector ordering method based on spectral purity-based criterion was adopted to overcome the problem of generating new constituents in EMP. A feature selection algorithm was employed to improve the efficiency of EMMP. Experiments were carried out on a hyperspectral data set collected by NASA's Airborne Visible-Infrared Imaging Spectrometer sensor. Experimental results showed that EMMP was effective at describing morphological information in hyperspectral data, and that this letter's method was superior to the previous method in terms of classification accuracy but inferior to the previous method in terms of time consumption.     

Hyperspectral Multiple Features Optimization Using Improved Firefly Algorithm

The utilization of hyperspectral remote sensing image is mainly based on the spectral information,and the spatial information is always be ignored.To solve this problem,a novel hyperspectral multiple features optimization approach based on improved firefly algorithm is presented.Firstly,four spatial features,the local statistical features,gray level co-occurrence matrix features,Gabor filtering features and morphological features of hyperspectral remote sensing image are extracted,and some spectral bands are selected and then combined with these spatial features,and the feature set is constructed.Then,the firefly algorithm is used to optimize the extracted features.In view of the slow convergence speed of firefly algorithm,we use the random inertia weight from particle swarm optimization algorithm to modifiy the location update formula of firefly algorithm,and JM(Jeffreys-Matusita)distance and Fisher Ratio are used as the objective function.Two urban hyperspectral datasets are used for performance evaluation,and the classification results derived from spectral information and spectral-spatial information are compared.The experiments show that random inertia weight can improve the speed of FA-based feature selection algorithm,the performance with multiple features is better than that of spectral information for urban land cover classification,The statistical results of the two sets of experimental data indicate that the selected number of morphological features are the most in the four spatial features.The local statistical features and morphological features are more helpful to the classification of hyperspectral remote sensing images than GLCM and Gabor features.     

Spectrally segmented principal component analysis of hyperspectral imagery for mapping invasive plant species

Principal component analysis (PCA) is one of the most commonly adopted feature reduction techniques in remote sensing image analysis. However, it may overlook subtle but useful information if applied directly to the analysis of hyperspectral data, especially for discriminating between different vegetation types. In order to accurately map an invasive plant species (horse tamarind, Leucaena leucocephala) in southern Taiwan using Hyperion hyperspectral imagery, this study developed a spectrally segmented PCA based on the spectral characteristics of vegetation over different wavelength regions. The developed algorithm can not only reduce the dimensionality of hyperspectral imagery but also extracts helpful information for differentiating more effectively the target plant species from other vegetation types. Experiments conducted in this study demonstrated that the developed algorithm performs better than correlation‐based segmented principal component transformation (SPCT) and conventional PCA (overall accuracy: 86%, 76%, 66%; kappa value: 0.81, 0.69, 0.57) in detecting the target plant species, as well as mapping other vegetation covers.     

高光谱数据特征选择与特征提取研究

高光谱遥感数据的最主要特点是: 传统图像维与光谱维信息融合为一体, 即“图谱合一”。针对高光谱数据波段多、数据量大、冗余度大等特点, 论述了特征选择和特征提取的若干算法, 分析了各自的优缺点。重点研究了导数光谱算法, 并针对二值编码的不足研究了其改进算法-- 四值编码算法。最后用编码技术和导数光谱技术提取了地物的光谱特征参数; 试验表明: 四值编码算法比二值编码算法效果更佳; 光谱导数阶数越高, 对地物特征的表达越有效。     

A precise approach to tracking dim-small targets using spectral fingerprint features

A precise method for accurately tracking dim-small targets, based on spectral fingerprint is proposed where traditional full color tracking seems impossible. A fingerprint model is presented to adequately extract spectral features. By creating a multidimensional feature space and extending the limited RGB information to the hyperspectral information, the improved precise tracking model based on a nonparametric kernel density estimator is built using the probability histogram of spectral features. A layered particle filter algorithm for spectral tracking is presented to avoid the object jumping abruptly. Finally, experiments are conducted that show that the tracking algorithm with spectral fingerprint features is accurate, fast, and robust. It meets the needs of dim-small target tracking adequately.