基于高光谱影像的地形图要素变化自动检测与更新方法研究

探讨采用高光谱遥感影像自动检测地形图变化的技术途径。针对地形图变化检测特点,利用经过辐射和几何处理的高光谱影像,结合已有地面资料确定地物样区,建立地形图要素的光谱特征。在建立地物要素光谱特征过程中,提出结合地形图资料,采用迭代光谱特征重建方法,利用训练样本和初始分析结果作为新的样本进行特征提取,克服高光谱影像处理中存在的训练样本数量要求大的难点,提高地物光谱特征建立的可靠性,从而有效提高分析精度。采用EO-1数据实验表明,该方法能够有效实现地物要素信息的自动提取,是变化检测与自动更新的一种有效方法。     

融合多光谱影像的高光谱影像厚云去除方法

云遮挡对高光谱影像的应用造成了不可忽视的影响。现有云去除方法通常利用时域近邻的同源影像提供辅助信息。然而,高光谱影像(如GF-5和EO-1高光谱影像)较低的时间分辨率导致同源辅助影像中可能存在较大的地物覆盖变化。时间分辨率更高的多光谱影像(如Landsat 8 OLI影像)能提供时间上更接近于高光谱云影像的辅助信息,从而减少地物覆被变化带来的影响。为应对高光谱和多光谱波段之间差异较大的问题,本文基于空谱随机森林(spatial-spectral-based random forest,SSRF)方法,提出一种利用多光谱影像(Landsat 8 OLI影像)对高光谱影像进行厚云去除的方法,将其简记为SSRF_M。SSRF_M较强的非线性拟合能力使其能够综合利用多光谱影像所有波段的有效数据对各个高光谱波段进行重建。本文使用GF-5和EO-1高光谱影像进行模拟云去除试验,视觉和定量评价结果均表明,与利用时间间隔更长的同源辅助影像的方法相比,本文方法能获得更高精度的云下信息重建结果。     

线性混合光谱模型高光谱压缩感知

高光谱压缩感知(HCS)对于解决机载或星载高光谱数据的存储与实时传输具有重要意义。目前,线性混合模型(LMM)已被成功应用于HCS;然而,由于光照条件、地形变化以及大气作用等的影响,所获取的地物光谱会发生扰动,从而限制了HCS重建质量的提高。在LMM基础上,通过引入光谱修正项来修正光谱扰动,提出了光谱扰动修正的LMM (SPC_LMM);在此基础上,进一步提出了基于SPC_LMM的HCS (HCS_SPC_LMM)方法。该方法在采样端仅对原始高光谱图像进行光谱维压缩采样,基于压缩采样数据,将SPC_LMM应用HCS的重建,利用交替方向乘子法(ADMM)分别估计SPC_LMM中各分量的最优值,以获得最优的高光谱图像重建质量。实验结果表明,HCS_SPC_LMM能够获得优于其他典型HCS方法的重建质量。     

应用型高光谱影像卷积神经网络分类方法

针对生产任务中常用的卫星高光谱数据空间分辨率不高、地物复杂的特点,提出一种实用性和灵活性较强、效率较高、不依赖空间信息的分类方法.对高光谱遥感影像数据进行分析,依据其光谱信息丰富,但在复杂地物中空间特征不足的实际情况,采用离散采样的方法,充分利用质量较好的样本点进行特征提取.对传统卷积神经网络进行改进,通过卷积层与池化层的重组等措施,使其更充分地利用地物的光谱特性.该方法在珠海一号高光谱影像上实现了对地物的有效分类.     

联合空-谱信息的高光谱影像深度三维卷积网络分类

针对高光谱影像分类高维和小样本的特点,提出一种基于深度三维卷积神经网络的高光谱影像分类方法。首先,该方法直接以高光谱数据立方体为输入,利用三维卷积操作提取高光谱数据立方体的三维空-谱特征。然后,利用残差学习构建深层网络,提取更高层次的特征表达,以提高分类精度。最后,采用Dropout正则化方法防止过拟合。利用Pavia大学、Indian Pines和Salinas 3组高光谱数据进行试验验证,结果表明,与支持向量机和现有的基于深度学习的高光谱影像分类方法相比,该方法能有效提高高光谱影像的地物分类精度。     

基于光谱特征参量化的高光谱影像精细分类

从高光谱影像能够提供地物连续光谱曲线的优势出发,提出了提取地物诊断性光谱吸收峰的特征参数进行地物精细分类的方法。用OMIS高光谱数据进行实验,首先对光谱曲线进行包络线去除处理,然后在归一化的曲线上提取光谱吸收峰的形态特征参数,根据不同种地物的光谱差异与分类需求进行特征参数选择,用于地物分层精细分类,在每一类别的地物之中实现不同子类的区分。分类总体精度达到81.022 6%,Kappa系数为0.748 9,尤其在植被和水体的子类区分上取得了较好的效果,证实了该方法的有效性。     

机载多光谱LiDAR数据的地物分类方法

机载多光谱LiDAR系统能够快速地获取大范围地表面上地物光谱和几何数据,并能够保证所获取的光谱与空间几何数据在空间和时间上相对完整和一致性。支持向量机（SVM）是一种基于小样本的学习方法,它避开了从归纳到演绎的传统分类过程。因此,本文提出了基于SVM多光谱LiDAR数据的地物目标分类方法。该方法首先将多个独立波段的LiDAR数据融合为单一的、包含多个波段信息的点云数据,然后将融合后的点云内插为距离影像和多光谱影像,最后利用SVM进行多光谱LiDAR数据的地物覆盖分类。通过对加拿大Optech公司的Titan机载多光谱LiDAR数据的试验证明：相对于传统的单波段LiDAR数据,多光谱LiDAR数据可以获得较好的地物分类精度;比较试验发现SVM分类方法适用于多光谱LiDAR数据的地物分类。     

基于非正定 OCSVM 的高光谱影像地物异常检测

针对基于高斯径向基核函数的OCSVM等异常检测算法,对地物光谱变异极为敏感,导致算法异常检测性能不稳定的问题,根据光谱角度余弦测度对光谱形状相似性的描述不受地物光谱辐射强度变异影响的特性,将具有非正定核特性的光谱角度余弦核测度引入非正定SVM算法中,提出一种基于非正定OCSVM的高光谱影像地物异常检测算法。利用四组模拟数据进行目标异常检测实验,结果表明,该算法能够有效检测出高光谱影像数据中的目标地物,检测精度提升明显。     

判别流形学习算法的高光谱数据降维与树种识别

高光谱遥感影像的波段光谱特征是各类地物内在物理化学性质的反映,在对不同地物进行分类与识别时具有巨大潜能,但由于其波段多造成的信息冗余,需要对高光谱数据进行有效降维,以提高高光谱影像的分类准确度。本文提出了基于判别局部片排列的流形学习算法（DLA）对Hypersion高光谱数据进行降维,通过对局部样本数据进行流形学习框架内的优化训练,将原始光谱特征空间转换为低维的最优判别流形子空间,然后在该子空间内利用最大似然分类器对Hypersion影像中的每个像素进行分类,并与主成分分析（PCA）、原始光谱特征（spectral）降维方法的分类效果进行比较。结果表明,DLA能够有效提高高光谱数据的分类准确度,对不同树种分类取得了满意效果。     

结合Gram-Schmidt变换的高光谱影像谐波分析融合算法

针对高光谱影像谐波分析融合(HAF)算法在影像融合时不顾及地物光谱曲线整体反射率这一缺陷,提出了结合Gram-Schmidt变换的高光谱影像谐波分析融合(GSHAF)改进算法。GSHAF算法可在完全保留融合前后像元光谱曲线波形形态的基础上,将高光谱影像融合简化为各像元光谱曲线的谐波余相组成的二维影像与高空间分辨率影像之间的融合。它是在原始高光谱影像光谱曲线被谐波分解为谐波余项、振幅和相位后,首先将其谐波余项与高空间分辨率影像进行GS变换融合,这样便可有效地修正融合后像元光谱曲线的反射率特征,随后再利用该融合影像与谐波振幅、相位进行谐波逆变换,完成高光谱影像谐波融合。本文最后利用Hyperion高光谱遥感影像与ALI高空间分辨率影像对GSHAF算法进行可行性分析,再以HJ-1A等卫星数据对其进行普适性验证,试验结果表明,GSHAF算法不仅可以完全地保留光谱曲线波形形态,而且融合后影像的地物光谱曲线反射率更接近真实地物。     

光谱遥感数据波形分析法的应用

本文利用傅立叶波形分析算法处理了光谱反射率数据,把不同地物的光谱分解为特征意义明确,信息内涵丰富,对各种地物可分辨性强的各次谐波分量。诸谐波分量的参数——波幅和初相唯一地决定了其对光谱曲线的贡献和构形位置。试验表明傅立叶波形分析算法可以成为处理各种成像光谱仪获取的超维图像的有效工具。     

绢云母的光谱特征变异分析及成像光谱地质成因信息提取

鉴于绢云母(和白云母)类粘土矿物对于研究与矿化紧密相关的中低温热液蚀变机制、与温压有关的地质成因过程等的重要意义,开展了绢云母的岩矿光谱特征变异分析和成像光谱矿物识别,并对岩石、矿物的试验室光谱和影像光谱所反映的地质成因信息的提取进行探讨。     

成像光谱矿物识别方法与识别模型评述

矿物识别和矿物填图是成像光谱应用最成功的领域之一。本文将国内外发展的矿物识别模型归纳为光谱匹配和以知识为基础的智能识别两大类型进行讨论。对光谱匹配方法分别从其方法的分类、光谱相似性测度、整体光谱匹配算法、局部光谱识别、亚像元光谱识别、混合像元分解和矿物端元选择、光谱减维和噪声弱化等方面作了评述。最后，讨论了矿物识别和填图研究中存在的主要问题，指出研究建立全谱段矿物识别方法和技术体系将是今后光谱矿物识别和矿物填图的重要发展方向。     

用卫星高光谱数据提取德兴铜矿区植被污染信息

在深入分析研究德兴铜矿矿区植被光谱特征的基础上,利用美国EO-1卫星Hyperion高光谱数据,通过反演表征植物生理状态的光谱特征参数(红边位置和最大吸收深度)变异,提取与污染相关的信息,获取了矿山植被污染生态效应概况,为矿山污染的诊断和监测提供新技术和知识支撑。     

基于光谱相似性的多光谱数据纹理编码算法研究

基于多光谱纹理“映射模式”概念，提出了基于光谱数据相似性的多光谱、高光谱数据的编码方法。利用光谱相似测度对不同类型的纹理进行编码，表征地物的全局纹理特征，将纹理提取的算法扩展到多维光谱图像分析中，提出了多尺度纹理组合算法。试验证明，该方法合理有效，可大大提高分类的准确性和精度。     

Lithological mapping from hyperspectral data by improved use of spectral angle mapper

The spectral angle mapper (SAM), as a spectral matching method, has been widely used in lithological type identification and mapping using hyperspectral data. The SAM quantifies the spectral similarity between an image pixel spectrum and a reference spectrum with known components. In most existing studies a mean reflectance spectrum has been used as the reference spectrum for a specific lithological class. However, this conventional use of SAM does not take into account the spectral variability, which is an inherent property of many rocks and is further magnified in remote sensing data acquisition process. In this study, two methods of determining reference spectra used in SAM are proposed for the improved lithological mapping. In first method the mean of spectral derivatives was combined with the mean of original spectra, i.e., the mean spectrum and the mean spectral derivative were jointly used in SAM classification, to improve the class separability. The second method is the use of multiple reference spectra in SAM to accommodate the spectral variability. The proposed methods were evaluated in lithological mapping using EO-1 Hyperion hyperspectral data of two arid areas. The spectral variability and separability of the rock types under investigation were also examined and compared using spectral data alone and using both spectral data and first derivatives. The experimental results indicated that spectral variability significantly affected the identification of lithological classes with the conventional SAM method using a mean reference spectrum. The proposed methods achieved significant improvement in the accuracy of lithological mapping, outperforming the conventional use of SAM with a mean spectrum as the reference spectrum, and the matching filtering, a widely used spectral mapping method.     

新疆哈密三种典型蚀变矿物的HyMap高光谱遥感信息提取

利用机载的可见光、近红外及短波红外成像光谱(ＨyMap)数据,对新疆哈密地区岩矿信息识别方法进行研究。基于方解石、 绿泥石和绢云母3种常见蚀变矿物的光谱特征,在遥感数据定标和反射率图像转换的基础上,应用光谱角度模型（SAM）分类法进行自 动匹配识别和信息提取。通过掩膜技术进行方解石、绿泥石和绢云母矿物填图,并结合实验室光谱数据库光谱进行了验证。     

Multiple spectral similarity metrics for surface materials identification using hyperspectral data

Modern hyperspectral imaging and non-imaging spectroradiometer has the capability to acquire high-resolution spectral reflectance data required for surface materials identification and mapping. Spectral similarity metrics, due to their mathematical simplicity and insensitiveness to the number of reference labelled spectra, have been increasingly used for material mapping by labelling reflectance spectra in hyperspectral data labelling. For a particular hyperspectral data set, the accuracy of spectral labelling depends considerably upon the degree of unambiguous spectral matching achieved by the spectral similarity metric used. In this work, we propose a new methodology for quantifying spectral similarity for hyperspectral data labelling for surface materials identification. Developed adopting the multiple classifier system architecture, the proposed methodology unifies into a single framework the differential performances of eight different spectral similarity metrics for the quantification of spectral matching for surface materials. The proposed methodology has been implemented on two types of hyperspectral data viz. image (airborne hyperspectral images) and non-image (library spectra) for numerous surface materials identification. Further, the performance of the proposed methodology has been compared with the support vector machines (SVM) approach, and with all the base spectral similarity metrics. The results indicate that, for the hyperspectral images, the performance of the proposed methodology is comparable with that of the SVM. For the library spectra, the proposed methodology shows a consistently higher (increase of about 30% when compared to SVM) classification accuracy. The proposed methodology has the potential to serve as a general library search method for materials identification using hyperspectral data.     

Use of field reflectance data for crop mapping using airborne hyperspectral image

Recent developments in hyperspectral remote sensing technologies enable acquisition of image with high spectral resolution, which is typical to the laboratory or in situ reflectance measurements. There has been an increasing interest in the utilization of in situ reference reflectance spectra for rapid and repeated mapping of various surface features. Here we examined the prospect of classifying airborne hyperspectral image using field reflectance spectra as the training data for crop mapping. Canopy level field reflectance measurements of some important agricultural crops, i.e. alfalfa, winter barley, winter rape, winter rye, and winter wheat collected during four consecutive growing seasons are used for the classification of a HyMAP image acquired for a separate location by (1) mixture tuned matched filtering (MTMF), (2) spectral feature fitting (SFF), and (3) spectral angle mapper (SAM) methods. In order to answer a general research question “what is the prospect of using independent reference reflectance spectra for image classification”, while focussing on the crop classification, the results indicate distinct aspects. On the one hand, field reflectance spectra of winter rape and alfalfa demonstrate excellent crop discrimination and spectral matching with the image across the growing seasons. On the other hand, significant spectral confusion detected among the winter barley, winter rye, and winter wheat rule out the possibility of existence of a meaningful spectral matching between field reflectance spectra and image. While supporting the current notion of “non-existence of characteristic reflectance spectral signatures for vegetation”, results indicate that there exist some crops whose spectral signatures are similar to characteristic spectral signatures with possibility of using them in image classification.     

Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm

Classification of hyperspectral images has been receiving considerable attention with many new applications reported from commercial and military sectors. Hyperspectral images are composed of a large number of spectral channels, and have the potential to deliver a great deal of information about a remotely sensed scene. However, in addition to high dimensionality, hyperspectral image classification is compounded with a coarse ground pixel size of the sensor for want of adequate sensor signal to noise ratio within a fine spectral passband. This makes multiple ground features jointly occupying a single pixel. Spectral mixture analysis typically begins with pixel classification with spectral matching techniques, followed by the use of spectral unmixing algorithms for estimating endmembers abundance values in the pixel. The spectral matching techniques are analogous to supervised pattern recognition approaches, and try to estimate some similarity between spectral signatures of the pixel and reference target. In this paper, we propose a spectral matching approach by combining two schemes—variable interval spectral average (VISA) method and spectral curve matching (SCM) method. The VISA method helps to detect transient spectral features at different scales of spectral windows, while the SCM method finds a match between these features of the pixel and one of library spectra by least square fitting. Here we also compare the performance of the combined algorithm with other spectral matching techniques using a simulated and the AVIRIS hyperspectral data sets. Our results indicate that the proposed combination technique exhibits a stronger performance over the other methods in the classification of both the pure and mixed class pixels simultaneously.