共查询到20条相似文献，搜索用时 15 毫秒
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Spectral library search is emerging as a viable approach for material identification and mapping by reusing spectral knowledge gained from hyperspectral remote sensing across space and time. The potential of retrieving meaningful spectral material identifications in the presence of reflectance of spectra of various material types and with various similarity metrics has been assessed in this study. Test reflectance spectra of various vegetation, minerals, soils and urban material types are identified by searching through the composite reflectance spectral library obtained by combining various institutional reflectance spectral libraries. The accuracy of material identifications under various conditions: (i) in the presence of identical, similar and dissimilar spectra; (ii) in the presence of only identical and dissimilar spectra; and (iii) in the presence of only dissimilar spectra has been assessed with several similarity metrics. Results indicate the possibility of obtaining 100% accurate material identifications by library search if the spectral library contains identical spectra. However, the presence of a large number of similar spectra, despite the presence of identical spectra, is found to increase false positives, thereby reducing the accuracy of retrievals to 82% at best. Further, the accuracy of material identifications in the presence of similar spectra is similarity metric-dependent and varied from about 52% (obtained from Binary Encoding) to 82% (obtained from Normalized Spectral Similarity Score). Overall, results support the possibility of using independent reflectance spectral libraries for material identification while calling for robust spectral similarity metrics. 相似文献
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Imaging spectroscopy is an emerging and versatile technique that finds applications in diverse fields concerned with remote identification, discrimination and mapping of materials. The large amount of spectral data produced by hyperspectral imaging necessitates the development of automated techniques that convert imagery directly into thematic maps. Spectral library search method, a method of choice for organic compound identification by the mass spectroscopy, has caught the attention of researchers as one of the appropriate methods for an efficient exploitation of high quality spectral data available from the hyperspectral imaging systems. Given the apparent increase in the number of papers appearing on the subject as well as the variety of methods proposed, it is reasonable to say that the field of automated interpretation of reflectance spectral data has passed its infancy now gaining important space in the scientific community. We present an overall view of the literature relevant to the development of library search method, the various search algorithms and systems available in the purview for developing an automated hyperspectral data analysis system for material identification. 相似文献
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Understanding the Unique Spectral Signature of Winter Rape 总被引：1，自引：0，他引：1
Driven by significant technological developments in the hyperspectral imaging, material mapping using reference spectra has received renewed interest of the remote sensing community. The applicability of reference spectral signatures in image classification depends mainly on the material type and its spectral signature behaviour. Identification and spectral characterization of materials which exhibit unique spectral behaviour is the first step in this approach. Consequently there have been active researches for the identification of surface materials which exhibit unique spectral signatures. The uniqueness of reflectance signature of winter rape relative to its co-occurring crop species was reported in this study. Reflectance spectral libraries constructed from field spectral reflectance measurements collected over five agricultural crops (alfalfa, winter barley, winter rape, winter rye, and winter wheat) during four subsequent growing seasons were classified by the linear discriminant analysis (LDA). Further, the reference field spectral database was used for the spectral feature fitting and classification of a historical HyMAP airborne hyperspectral imagery acquired at a separate site, by spectral library search. Results indicate the existence of a meaningful spectral matching between image and field spectra for winter rape and demonstrate the potential for transferring spectral library for hyperspectral image classification. The observed consistency in the discrimination of winter rape demonstrates experimentally the fundamental principle of remote sensing which suggests the theoretical existence of unique spectral signatures for materials which can be incorporated as reference spectral signatures for hyperspectral image classification. 相似文献
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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. 相似文献
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Kumar Arun Prasad Vaithilingam Selvam Ramasamy Ramasubramanian Chandra Sekar Kar 《国际地球制图》2015,30(5):580-599
Development of a spectral library is a prerequisite for the higher order classification of satellite data and hyperspectral image analysis to map any ecosystem with rich diversity. In this study, sampling methodology, collection of field and laboratory spectral signatures and post-processing methodologies were investigated for developing an exclusive spectral library of mangrove species using hyperspectral spectroscopic techniques. Canopy level field spectra and leaf level laboratory spectra were collected for 34 species (25 true and 9 associated mangroves) from two different mangrove ecosystems of the Indian east coast. Post-processing steps such as removal of water vapour absorption bands, correction of drifts which occur due to the thermal properties of the instrument during data collection and smoothing of spectra for its further utilisation were applied on collected spectra. The processed spectra were then compiled as spectral library. 相似文献
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提出了一种新型光谱相似性测度及其参数的自适应选择方法,并且将其应用到了高光谱影像地物检测中。由于这种相似性测度基于光谱角度余弦(SAC),因此在理论上对因光照强度变化、阴影和遮挡等引起的同种地物光谱变化的适应性较强。最后利用两幅高光谱影像进行了实验分析,实验结果证明提出的方法不仅能扩大阈值取值区间,而且可提高检测的精度。 相似文献
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辐射归一化旨在减小不同时相遥感影像间因获取条件不一致而导致的非地表辐射变化的差异,是土地覆盖变化监测的重要前提条件。本文根据高光谱图像上同类地物的谱形及数值的相似性,利用光谱角距离(SAD)和欧氏距离(ED)双重判定选取不变特征点,提出了一种基于光谱角—欧氏距离的辐射归一化方法。在评价指标中除了常用的均方根误差和相对偏差,更增加了高光谱特色的衡量光谱保真性指标:皮尔森系数、光谱扭曲程度。利用高光谱遥感CHRIS图像对本文提出方法进行验证,并与基于多元变化检测(MAD)的辐射归一化方法比较。结果表明,本文方法不仅在辐射特性上优于基于多元变化检测(MAD)的方法,而且具有保持光谱特性的优势,具有较好的应用前景。 相似文献
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In recent years hyperspectral imaging has proved its significance in the detection and mapping of various objects of interest in a scene. Various methods for object detection in hyperspectral images have been developed with their advantages and limitations. In the present study, a methodology comprising spectral derivative (first order) and spectral information divergence has been investigated for detection of objects in hyperspectral images. The efficacy of the detection scheme has been examined over two different hyperspectral data sets of Hyperion images. Tea plants (Camellia sinensis) and Sal trees (Shorea robusta) (pure pixels) have been detected as the objects of interest in the hyperspectral images independently with reduced false pixels. The proposed methodology may in future be applied for classification of mixed pixels. 相似文献
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Rama Rao Nidamanuri Bernd Zbell 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(5):683-691
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. 相似文献
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Mountain Glaciers are natural resources of fresh water and these affect the stream flow of the rivers, regional climate and further global climate. Observed trends and projected future evolutions of climate and Cryospheric variables clearly suggest a need to monitor these changes. Accordingly, the article presents the glacier features mapping using Hyperspectral remote sensing imagery. A freely available Hyperion satellite imagery acquired over Gepang Gath glacier in Himachal Pradesh, India is used for the study. Each class is identified based on their surface characteristics of spectral reflectance properties. Identification is simplified by demarcating the study glacier into accumulation and ablation areas through snowline. Accumulation area is characterized with high reflectance clean snow/ice and reduced moderate reflectance Snow/firn. The identification of classes in Hyperion imagery is validated using the spectral library from USGS and ASTER, and field spectra obtained from literature. 相似文献
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空间与谱间相关性分析的NMF高光谱解混 总被引：2，自引：1，他引：1
非负矩阵分解(NMF)技术是高光谱像元解混领域的研究热点。为了充分利用高光谱图像中丰富的空间与光谱相关性特征,改善基于NMF的高光谱解混算法性能,提出一种结合了空间与谱间相关性分析的NMF解混算法。算法针对NMF的通用性和局部极小问题,引入并结合高光谱图像两种典型的相关性特征,具体包括:基于马尔可夫随机场(MRF)模型,建立描述相邻像元空间相关特征的约束;通过复杂度映射技术,建立描述相邻波段谱间相关(光谱分段平滑)特征的约束;并将上述两种约束同时引入NMF解混目标函数中。实验结果表明,对于一般自然地物场景或人造地物场景,相对于分段平滑和稀疏约束的非负矩阵分解(PSNMFSC)、交互投影子梯度的非负矩阵分解(APSNMF)和最小体积约束的非负矩阵分解(MVCNMF)这3种代表性NMF解混参考算法,该算法可进一步提高高光谱解混精度;对于空间相关或谱间相关特征中某一种不显著的特殊场景,也具有更好的适应能力。通过将空间相关和谱间相关特征相结合,较全面地反映了高光谱数据与解混相关的重要特征,能够对绝大多数真实高光谱数据进行高精度解混,对高光谱解混及后续应用领域相关研究均具有参考价值。 相似文献
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矿物的混合多属于致密型混合,在可见光—短波红外波段的混合呈现非线性特征,同时由于矿物混合的复杂性以及图像中完全纯净的像元可能不存在等原因,使得从图像上提取端元具有较大不确定性。本文根据矿物单次散射反照率的线性可加性,提出一种基于矿物单次散射反照率光谱库的稀疏解混算法,利用Hapke模型将矿物反射率转换成矿物单次散射反照率,构建矿物单次散射反照率光谱库,以半监督的方式通过稀疏回归的方法从光谱库中寻找最优端元组合,并估算混合像元中各端元的丰度。利用RELAB矿物混合光谱库进行算法验证,结果表明,丰度反演的平均绝对误差为3.12%;将本文方法应用于美国内华达州铜矿区的AVIRIS高光谱图像数据,所得丰度图与美国地质勘探局USGS矿物识别结果具有较好的一致性。本文算法不需要从图像提取端元,并且考虑到了矿物的非线性混合特征,能够得到较高的反演精度,在近地行星和卫星表面岩矿成分的探测等领域具有较好的应用前景。 相似文献
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优化子空间SVM集成的高光谱图像分类 总被引：2，自引：0，他引：2
随机子空间集成是很有前景的高光谱图像分类技术,子空间的多样性和单个子空间的性能与集成后的分类精度密切相关。传统方法在增强单个子空间性能的同时,往往会获得大量最优但相似的子空间,因而减小它们之间的多样性,限制集成系统的分类精度。为此,提出优化子空间SVM集成的高光谱图像分类方法。该方法采用支持向量机(SVM)作为基分类器,并通过SVM之间的模式差别对随机子空间进行k-means聚类,最后选择每类中J-M距离最大的子空间进行集成,从而实现高光谱图像分类。实验结果显示,优化子空间SVM集成的高光谱图像分类方法能够有效解决小样本情况下的Hughes效应问题;总体精度达到75%–80%,Kappa系数达到0.61–0.74;比随机子空间集成方法和随机森林方法分类精度更高、更稳定,适合高光谱图像分类。 相似文献
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由于物体表面的空间分布通常是富有规律且局部连续的,在高光谱影像分类中应充分利用其光谱和空间信息。本文在对高光谱影像立方体进行降维处理的基础上,提出了一种联合空域和谱域信息的高光谱影像高效分类方法。首先,分别选用主成分分析(Principal Component Analysis,PCA)和正交投影波段选择(Orthogonal Projection Band Selection,OPBS)两种方法对原始高光谱数据进行预处理,获取降维后的影像数据。然后在其基础上提取扩展形态学特征(Extended Morphology Profiles,EMP)和地物表面纹理特征,组成联合光谱和纹理、形状结构特征。最后,采用支持向量机(Support Vector Machine,SVM)分类器对联合特征进行分类。针对不同真实高光谱数据集的实验结果表明,本文提出的方法运算效率高且具有令人满意的分类性能。 相似文献
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地面成像光谱数据的田间杂草识别 总被引：5，自引：0，他引：5
地面成像光谱数据兼具高光谱分辨率与高空间分辨率,在田间杂草识别中具有很好的应用前景。目前基于机器视觉的杂草识别方法以形状特征为主,当作物杂草形态相似时识别的困难和利用高光谱特征以像元为单元识别时效率较低,不利于实时自动化除草,因此,本文提出一种综合面向对象与高光谱特征匹配的杂草识别方法,在对作物杂草对象样本的形状特征和光谱曲线提取分析的基础上,建立基于形状特征规则与光谱角匹配的植物对象识别决策树,用于识别实验田中的作物杂草对象。实验结果表明,当场景中某些不同种类植物对象的形态相似时,基于形状特征规则与光谱角匹配的杂草识别方法可借助高光谱特征精细区分植物对象的种类,且在形状特征规则约束下使用高光谱特征匹配法识别植物对象,可克服"同物异谱"和"同谱异物"现象带来的不确定性,该方法识别精度可优于仅使用光谱角匹配法的情况,并优于使用颜色和形状分析技术的情况。 相似文献
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Effectiveness of SID as Spectral Similarity Measure to Develop Crop Spectra from Hyperspectral Image
Hasmukh J. Chauhan B. Krishna Mohan 《Journal of the Indian Society of Remote Sensing》2018,46(11):1853-1862
The present study was undertaken with the objective to check effectiveness of spectral information divergence (SID) to develop spectra from image for crop classes based on spectral similarity with field spectra. In multispectral and hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to develop crop spectra from the image itself. Hence, in this study methodology suggested to develop spectra for crops based on SID. Absorption features are unique and distinct; hence, validation of the developed spectra is carried out using absorption features by comparing it with field spectra and finding average correlation coefficient r?=?0.982 and computed SID equivalent r?=?0.989. Effectiveness of developed spectra for image classification was computed by probability of spectral discrimination (PSD) and resulted in higher probability for the spectra developed based on SID. Image classification was carried out using field spectra and spectra assigned by SID. Overall classification accuracy of the image classified by field spectra is 78.30% and for the image classified by spectra assigned through SID-based approach is 91.82%. Z test shows that image classification carried out using spectra developed by SID is better than classification carried out using field spectra and significantly different. Validation by absorption features, effectiveness by PSD and higher classification accuracy show possibility of new approach for spectra development based on SID spectral similarity measure. 相似文献
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In geological imaging spectrometry (i.e., hyperspectral remote sensing), surface compositional information (e.g., mineralogy and subsequently chemistry) is obtained by statistical comparison (by means of spectral matching algorithms) of known field- or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been given to comparison of the performance of the various spectral matching algorithms. Four spectral measures are presented: three that calculate the angle (spectral angle measure, SAM), the vector distance (Euclidean distance measure, ED) or the vector cross-correlation (spectral correlation measure, SCM), between a known reference and unknown target spectrum and a fourth measure that measures the discrepancy of probability distributions between two pixel vectors (the spectral information divergence, SID). The performance of these spectral similarity measures is compared using synthetic hyperspectral and real (i.e., Airborne Visible Infrared Imaging Spectrometer, AVIRIS) hyperspectral data of a (artificial or real) hydrothermal alteration system characterised by the minerals alunite, kaolinite, montmorillonite and quartz. Two statistics are used to assess the performance of the spectral similarity measures: the probability of spectral discrimination (PSD) and the power of spectral discrimination (PWSD). The first relates to the ability of the selected set of spectral endmembers to map a target spectrum, whereas the second expresses the capability of a spectral measure to separate two classes relative to a reference class. Analysis of the synthetic data set (i.e., simulated alteration zones with crisp boundaries at 1–2 nm spectral resolution) shows that (1) the SID outperforms the classical empirical spectral matching techniques (SAM, SCM and ED), (2) that SCM (SID, SAM and ED do not) exploits the overall shape of the reflectance curve and hence its outcomes are (positively and negatively) affected by the spectral range selected, (3) SAM and ED give nearly similar results and (4) for the same reason as in (2), the SCM is also more sensitive (again in positive and negative sense) to the spectral noise added. Results from the study of AVIRIS data show that SAM yields more spectral confusion (i.e., class overlap) than SID and SCM. In turn, SID is more effective in mapping the four target minerals than SCM as it clearly outperforms SCM when the target mineral coincides with the mineral phase on the ground. 相似文献