典型地物光谱空间结构特征与基本统计参数分析

分别从光谱曲线及光谱特征空间入手,对内蒙古突泉与青海同仁研究区野外实测岩石、土壤和植被等3类典型地物的光谱数据进行了分析,发现研究区无论在光谱曲线空间,还是在光谱特征空间,同类地物的分布形态基本一致,而不同之处是由不同地区的地质地貌、生态环境与气候因素决定的,但不影响其空间分布形态的整体特征.通过分析与实验,发现了几种能够在光谱特征空间对上述3类特征地物进行较好区分的波段组合,可以利用这几种波段组合指导根据遥感图像生成的二维散点图对这3类地物的分类,有助于改进遥感蚀变信息提取方法.     

成像光谱遥感技术及其图像光谱信息提取的分析研究

成像光谱技术是80年代发展起来的最新遥感方法。本文对其原理、理论基础及图像光谱信息提取的方法进行了探讨,并在红外细分光谱(FIMS)金矿蚀变带信息提取分析研究的基础上,通过对可见光细分19波段AMSS、澳大利亚的24波段GEOSCAN、MKII AMSS及美国GER64通道成像光谱数据的初步处理,发展和形成了一些针对超多波段成像光谱数据的图像处理和分析及光谱信息提取的方法。 成像光谱信息提取的方法,主要包括图像光谱反射率转换技术、图像光谱曲线显示、光谱特征参数测度(光谱吸收特征的波长位置、宽度、深度)、图像地物光谱曲线与地物光谱数据库的信息匹配以及地物光谱识别专家系统。本文以红外细分光谱图像在金矿蚀变带信息提取分析中的应用为例,讨论了成像光谱图像的一种处理分析技术及其发展前景。     

基于PROBA/CHRIS影像的归一化阴影植被指数NSVI构建与应用效果

开展高光谱遥感影像的阴影检测研究有助于去除阴影,并进一步发挥其高光谱分辨率优势.以多角度高光谱影像PROBA/CHRIS为数据源,尝试从增大明亮区植被、阴影区植被、水体区3种典型地物间光谱的差异入手,利用连续投影算法(successive projection algorithm,SPA)选取特征波段,并分析典型地物在...     

遥感蚀变信息检测中背景与干扰问题研究

在不同自然景观区的遥感蚀变信息检测中,背景与干扰是2个复杂而又容易混淆的研究对象。本文结合光谱混合模型理论与随机变量概率分布相关理论,深入分析了遥感数据的光谱空间几何结构特征(重点剖析二维散点图),系统研究了背景与干扰的关系、复杂度以及干扰地物类型的划分等问题。这些问题的探讨为遥感蚀变信息检测的最佳方案设计和蚀变信息潜在性评价与准确提取提供了技术思路。     

高光谱遥感异常信息在阿尔金索拉克地区铜金矿找矿工作中的应用

利用CASI/SASI机载高光谱遥感数据,对新疆阿尔金成矿带索拉克一带的蚀变矿物异常信息进行提取和分析,在此基础上总结区内蚀变矿物异常的分布规律及成因,结合典型岩石、矿物的地面光谱测量对不同地质体蚀变矿物的光谱曲线特征进行分析和总结,并选取索拉克铜金矿床的矿化蚀变地质剖面开展光谱测量分析,构建该区标志性蚀变矿物组合,建...     

基于峰值权重的岩心高光谱矿化蚀变信息提取

针对传统的光谱角制图(spectral angle mapping,SAM)方法在岩心高光谱矿化蚀变信息提取中的局限性,提出了一种基于光谱特征区间吸收峰值权重的高光谱蚀变信息提取方法。首先,在图像反射率转换、噪声去除和端元提取基础上,系统分析了岩心高光谱图像中与铀成矿密切相关的伊利石化、绿泥石化和碳酸盐化等3种铀矿化蚀变的诊断性光谱吸收特征;然后,分别选取同类蚀变光谱曲线之间差异较小的部分作为特征区间,并对特征区间内峰值处的1个波段的反射率设置权重;最后对分别添加权重的参考端元与图像像元进行特征区间内的光谱角计算,实现3种典型铀矿化蚀变信息的提取。该方法突出了吸收峰值和局部光谱信息,能够很好地对同一蚀变进行聚类,区分不同蚀变种类。精度验证和对比结果表明,该方法在权重系数ω=2时,岩心高光谱矿化蚀变信息提取精度可提高20%以上,应用效果显著。     

空间U统计量法在遥感蚀变信息提取中的应用研究

鉴于传统蚀变异常信息提取方法的局限性,试图在二维光谱特征空间(即散点图)中研究遥感蚀变信息的提取方法.在二维散点图中,两个波段灰度值的联合分布体现了各向异性的特点,聚类常呈类似椭圆形分布.对散点图中点的频数使用空间U统计量法逐个获得地物聚类的椭圆参数,将每个椭圆内的点映射到遥感图像上,经过目视解译,最终获得蚀变异常信息...     

高光谱遥感数据的DNA计算分类

提出了一种基于DNA计算的高光谱遥感数据光谱匹配分类新方法。该方法利用DNA编码提取各类地物光谱所携带的物理吸收与反射特征信息,将地物光谱特征转换为DNA编码空间特征,通过DNA计算基因操作寻找各类地物最典型的DNA信息链。在此基础上,利用DNA计算原理建立一系列模糊规则,对高光谱数据进行光谱匹配分类。通过与传统的光谱匹配算法(二值编码,光谱角,光谱差分特征编码)的分类结果进行比较,证明该算法分类精度优于传统高光谱数据的光谱匹配分类方法,具有实用价值。     

遥感岩矿信息提取基础与技术方法研究

在遥感地质应用中 ,对岩矿光谱和空间分布精细特征的探测是高空间与高光谱分辨率遥感的优势所在。随着传感器性能的改进 ,尤其是光谱分辨率的提高 ,改善了信息识别与提取的技术环境。本文从分析岩矿的光谱特征与遥感光谱识别规则出发 ,根据不同类型遥感数据的光谱特征 ,尤其是成像光谱数据丰富的光谱信息 ,研究不同尺度下遥感岩矿识别技术 ,建立相应的遥感应用模型 ,讨论了遥感技术集成。具体研究内容有 :(1)分析与总结遥感光谱识别规则。分别从矿物离子、矿物蚀变类型和蚀变矿物组合 3个层次分析了岩矿作用过程中矿物特征光谱的变化与变异…     

光谱分辨率对地物分辨能力的影响分析

对高光谱数据进行地物识别处理时,如果直接采用原始观测值进行分析,由于相邻波段之间相关性强,进行地物识别时需要大量的训练样本,而且对于一些分布较少的地物类别则无法处理,因此需要通过特征选择来消除数据中存在的冗余,提高数据分析效率。本文针对在不同光谱分辨率条件下,对典型地物分辨能力的统计,分析了光谱分辨率对地物自动识别能力的影响情况。同时通过模拟数据的办法比较了高光谱数据和多光谱数据(以TM数据为例)对地物光谱特征的描述能力,指出高光谱数据存在的优势。通过本文的研究,为高光谱遥感技术在地物自动识别方面的应用提出了初步的方向。     

烃类微渗漏的光谱遥感信息

烃类微渗漏的间接证据,包括一系列地球物理、地球化学、岩石矿物和地貌异常以及以后认识到的在土壤化学与植物生理方面引起的变异。所有这些现象均与烃类微渗漏密切相关。根据这种地表“蚀变体”的波谱特征,可利用遥感技术在特定波段内将其信息直接提取出来。     

Extraction of Alteration Mineral Information from Moderate Remote Sensing Images Using MPS Method

Although alteration minerals related to metallogenesis is very important in mineral exploration, information of alteration mineral is weakly expressed in remote sensing imagery, which is often subject to interfering noise and sometimes limited in spectral and spatial resolutions. Because of easy access, moderate images are the main sources of alteration mineral information. Therefore, it is very important to develop alteration mineral information extraction methods from remote sensing images. In this paper, a combined method based on Mask, principal component analysis (PCA) and support vector machine method (SVM) was used to extract alteration mineral information from Enhanced thematic mapper plus remote sensing data with limited spectral and spatial resolutions. First, a mask image of the remote sensing imagery was created to remove interference information such as vegetation, shadow and water. Then, PCA was employed to collect sample data relating to iron, argillic, and carbonatization alteration. Finally, SVM was used to deal with alteration anomaly and build a feature extraction model of high accuracy. The Mask-PCA-SVM model is used to extract alteration mineral information from remote sensing images of Hatu area, Xinjiang Uygur Autonomous Regions, China. The results show that the new methods proposed in this paper can coincide well with known deposits occurrences, rate reached 86.51%. While, the consistent rate with known deposits of the ratio model, PCA model and Spectral angle mapper model were only 3.37, 65.08 and 69.05% respectively. This suggests that the proposed model can find the actual distribution of mineral deposits more effectively by reducing interference to a greater degree.     

融合无人机影像与LiDAR点云的山区地表覆被景观特征探测

机载LiDAR数据能够准确提供对象的三维空间位置信息,无人机高分辨影像具备丰富的色彩信息与纹理信息,综合两种数据的优点,可进行数据集成融合。针对山区普遍存在的分布广泛的植被覆盖类型基质景观,本文通过构建可见光植被指数（VDVI）融合光谱信息点云数据,进行典型植被特征提取的研究。为了验证该方法提取信息的准确度,分别构建了3种数据源并依次进行山区地表植被提取试验。对试验结果定性定量分析表明,融合光谱点云数据的植被覆被率为56.8%,较另外两种数据类型的植被覆被率更加接近参考值（58.2%）,可信度相对较高,效果更好,植被图斑轮廓更加清晰,更适用于目标对象植被特征提取,使融合影像信息的点云数据分类优势得以体现,证实了该方法面向山区植被特征提取的可行性。     

Improved k-means and spectral matching for hyperspectral mineral mapping

Mineral mapping is an important step for the development and utilization of mineral resources. The emergence of remote sensing technology, especially hyperspectral imagery, has paved a new approach to geological mapping. The k-means clustering algorithm is a classical approach to classifying hyperspectral imagery, but the influence of mixed pixels and noise mean that it usually has poor mineral mapping accuracy. In this study, the mapping accuracy of the k-means algorithm was improved in three ways: similarity measurement methods that are insensitive to dimensions are used instead of the Euclidean distance for clustering; the spectral absorption features of minerals are enhanced; and the mineral mapping results are combined as the number of cluster centers (K) is incremented from 1. The improved algorithm is used with combined spectral matching to match the clustering results with a spectral library. A case study on Cuprite, Nevada, demonstrated that the improved k-means algorithm can identify most minerals with the kappa value of over 0.8, which is 46% and 15% higher than the traditional k-means and spectral matching technology. New mineral types are more likely to be found with increasing K. When K is much greater than the number of mineral types, the accuracy is improved, and the mineral mapping results are independent of the similarity measurement method. The improved k-means algorithm can also effectively remove speckle noise from the mineral mapping results and be used to identify other objects.     

中等植被覆盖区金矿蚀变TM及JERS－1OPS遥感信息增强技术

本文分析了与金矿化相伴的蚀变矿物(铁氧化物、粘土矿物、碳酸盐矿物)的反射光谱吸收特征及金矿区上覆植被反射光谱对金矿化蚀变信息的干扰,同时介绍了利用植被指数法、比值-主成份变换法和植被掩模法对陆地卫星TM、JERS-1OPS等遥感图像进行处理,压抑植被反射光谱干扰信息和增强金矿化蚀变反射光谱信息的方法和效果。     

基于植物波谱特征的城市环境效应研究

在苏州城市环境地球化学特征研究基础上,利用苋菜、四季豆及油菜等3种不同植物的光谱特征,研究苏州城市环境效应。研究结果显示,在苏州土壤环境污染区域,植物光谱呈现可见光绿光波段反射率升高、红谷变浅、红边蓝移等变化特征;苋菜在3种植物中可成为植物光谱效应用于城市环境地球化学质量研究的理想指示植物。     

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。     

Scaling dimensions in spectroscopy of soil and vegetation

The paper revises and clarifies definitions of the term scale and scaling conversions for imaging spectroscopy of soil and vegetation. We demonstrate a new four-dimensional scale concept that includes not only spatial but also the spectral, directional and temporal components. Three scaling remote sensing techniques are reviewed: (1) radiative transfer, (2) spectral (un)mixing, and (3) data fusion. Relevant case studies are given in the context of their up- and/or down-scaling abilities over the soil/vegetation surfaces and a multi-source approach is proposed for their integration.Radiative transfer (RT) models are described to show their capacity for spatial, spectral up-scaling, and directional down-scaling within a heterogeneous environment. Spectral information and spectral derivatives, like vegetation indices (e.g. TCARI/OSAVI), can be scaled and even tested by their means. Radiative transfer of an experimental Norway spruce (Picea abies (L.) Karst.) research plot in the Czech Republic was simulated by the Discrete Anisotropic Radiative Transfer (DART) model to prove relevance of the correct object optical properties scaled up to image data at two different spatial resolutions. Interconnection of the successive modelling levels in vegetation is shown. A future development in measurement and simulation of the leaf directional spectral properties is discussed.We describe linear and/or non-linear spectral mixing techniques and unmixing methods that demonstrate spatial down-scaling. Relevance of proper selection or acquisition of the spectral endmembers using spectral libraries, field measurements, and pure pixels of the hyperspectral image is highlighted. An extensive list of advanced unmixing techniques, a particular example of unmixing a reflective optics system imaging spectrometer (ROSIS) image from Spain, and examples of other mixture applications give insight into the present status of scaling capabilities.Simultaneous spatial and temporal down-scaling by means of a data fusion technique is described. A demonstrative example is given for the moderate resolution imaging spectroradiometer (MODIS) and LANDSAT Thematic Mapper (TM) data from Brazil. Corresponding spectral bands of both sensors were fused via a pyramidal wavelet transform in Fourier space. New spectral and temporal information of the resultant image can be used for thematic classification or qualitative mapping.All three described scaling techniques can be integrated as the relevant methodological steps within a complex multi-source approach. We present this concept of combining numerous optical remote sensing data and methods to generate inputs for ecosystem process models.     

Comparison of the Different Classifiers in Vegetation Species Discrimination Using Hyperspectral Reflectance Data

Feature selection methods play an important role in Hyperspectral Remote Sensing applications, especially in classification. This paper proposed a new Feature selection strategy for Hyperspectral dataset. This strategy was designed to help refine vegetation classification of 4 categories with 13 species vegetation which are the most common species in central China. An ASD field spectrometer (Analytical Spectral Device) was used to collect spectrum information of plant leaves from each species through 400 nm to 900 nm with 1 nm spectral resolution. Firstly, correlation between the physical/chemical characteristics of the leaves and the separability of each vegetation species was tested. Then, two feature selection methods, spectral angle and spectral distance, and the feature parameters extracted from spectral curves (FPESC) were used to build the feature space which would be the input space for the classifiers. At last, two linear classifiers, mahalanobis distance (MDC), and fisher linear discriminate analysis (FLDA), and a quadratic classifier, maximum likelihood (MLC), were used for vegetation species refine classification. The results showed that (1) there were no significant differences among 13 species on the leaf dry weight (physical parameter) and leaf chlorophyll content (chemical parameter); (2) FPESC of 13 species have distinctive differences and could be ideal features to discriminate these species; (3) The linear classifiers, MDC and FLDA, have better classification results in the experiments compared to the quadratic classifier MLC, where MDC has the highest classification accuracy which is above 96.2 %.