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101.
102.
高光谱遥感技术是近年来兴起并逐渐成熟的蚀变矿物研究方法。盘龙沟金矿床围岩蚀变发育,主要蚀变类型包括硅化、绿泥石化、绢云母化、碳酸盐化、钠长石化、褐铁矿化等,蚀变与金矿化关系密切。文章通过航空高光谱遥感数据和地面光谱数据,提取和诊断盘龙沟金矿床的不同地段、不同地质体的蚀变矿物信息,研究分析金矿区的航空和地面高光谱蚀变矿物、蚀变矿物组合特征,以及矿区围岩蚀变特征,总结航空和地面光谱蚀变矿物分带找矿标志规律,结合金矿床地质矿产特征,系统剖析金矿床的综合找矿标志特征,构建盘龙沟金矿床高光谱蚀变矿物找矿标志;同时依据绢云母、绿泥石的诊断光谱,研究两种蚀变矿物的光谱变化特征,分析了金矿床围岩蚀变的形成条件和蚀变矿物的温压条件,进而初步推测盘龙沟金矿床为中低温热液型金矿,其矿体与围岩形成的环境条件不同。 相似文献
103.
近20年来,高光谱遥感技术(Hyperspectral Remote Sensing)发展迅速,已成为遥感技术的前沿,而矿产资源评价与矿山环境监测是高光谱遥感应用中最成功的领域。高光谱遥感技术具有分辨率高的特点,通过对矿物种类、丰度和成分的识别,特别是对与成矿作用、矿山污染源密切相关的蚀变矿物的识别,可以有效地圈定热液矿化蚀变带,定量或半定量地估计相对蚀变强度和蚀变矿物含量,从而进行岩石矿物的分类和填图,并为矿产资源评价与矿山环境监测提供靶区,指导进一步找矿勘探和环境监测工作的开展。介绍了高光谱遥感技术在矿产资源评价与矿山环境监测中的应用原理和成像光谱学的逐步完善,并详细列举了高光谱遥感技术在斑岩铜矿、热液型金矿、地浸砂岩型铀矿、油气、金刚石等矿种的找矿方面,以及在矿山废弃物中生成酸和缓冲酸分布的监测等方面的具体案例,以期为中国矿产资源评价与矿山环境监测中高光谱遥感技术的应用提供借鉴。 相似文献
104.
We conducted a land change analysis to determine if the forest communities of presettlement and present contain areas that are analogous in composition, using surveys of the Holland Land Company (1797–1799) and U.S. Forest Service (1991–1993) from western New York. Gridded forest-type maps are produced from each survey using two models: a uniform model that assumes each data grid cell is occupied by a single forest type, and a mixture model in which grid cells are assumed to be occupied by multiple forest types in different proportions. The mixture model consistently detects a larger area of analogous communities in the two time periods at both global and local scales. 相似文献
105.
机载LiDAR和高光谱数据融合提取冰川雪线 总被引:1,自引:0,他引:1
以西藏那曲县境内的“中习一号”冰川为研究区,对2011年8月获取的机载激光雷达点云进行预处理和滤波分类,提取研究区数字高程模型(digital elevation model,DEM);将DEM数据分别与同期获取的机载高光谱栅格数据和提取出的冰川矢量数据进行三维地形模拟,利用DEM数据对高光谱最大似然法分类结果进行正射纠正,从而获取研究区的数字正射影像(digital orthophoto map,DOM);最后结合研究区DOM和机载点云数据提取“中习一号”冰川的雪线.结果表明:融合机载高光谱和机载激光雷达2种数据的优势,能更方便地提取出冰川雪线,而且能很好地显示雪线的高度. 相似文献
106.
It is necessary to estimate heavy metal concentrations within soils for understanding heavy metal contaminations and for keeping the sustainable developments of ecosystems. This study, with the floodplain along Le’an River and its two branches in Jiangxi Province of China as a case study, aimed to explore the feasibility of estimating concentrations of heavy metal lead (Pb), copper (Cu) and zinc (Zn) within soils using laboratory-based hyperspectral data. Thirty soil samples were collected, and their hyperspectral data, soil organic matters and Pb, Cu and Zn concentrations were measured in the laboratory. The potential relations among hyperspectral data, soil organic matter and Pb, Cu and Zn concentrations were explored and further used to estimate Pb, Cu and Zn concentrations from hyperspectral data with soil organic matter as a bridge. The results showed that the ratio of the first-order derivatives of spectral absorbance at wavelengths 624 and 564 nm could explain 52% of the variation of soil organic matter; the soil organic matter could explain 59%, 51% and 50% of the variation of Pb, Cu and Zn concentrations with estimated standard errors of 1.41, 48.27 and 45.15 mg·kg?; and the absolute estimation errors were 8%–56%, 12%–118% and 2%–22%, and 50%, 67% and 100% of them were less than 25% for Pb, Cu and Zn concentration estimations. We concluded that the laboratory-based hyperspectral data hold potentials in estimating concentrations of heavy metal Pb, Cu and Zn in soils. More sampling points or other potential linear and non-linear regression methods should be used for improving the stabilities and accuracies of the estimation models. 相似文献
107.
Clonal selection feature selection algorithm (CSFS) based on clonal selection algorithm (CSA), a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm (FCSFS) for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS), respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery. 相似文献
108.
109.
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
110.
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