共查询到17条相似文献，搜索用时 203 毫秒
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从岩石光谱出发,结合光谱谱带强度特征和光谱波形特征,针对机载热红外高光谱数据（TASI）,在以往算法基础上,提出一种改进的算法--光谱离散能级波形匹配法（SDEM）,并将其运用到岩性分类研究中。SDEM算法能识别岩石光谱间的微小差异,并在充分考虑光谱谱带强度和波形特征的同时,有效减弱数据噪声。与传统的岩性分类方法--高光谱角度制图法（SAM）相比,改进的算法能更精确地区分岩石相似光谱,识别易混淆岩性,对出现“异物同谱”现象的岩石也具有更好的区分能力。将SDEM、SAM方法应用于甘肃柳园地区TASI数据岩性分类研究中,可看出SDEM方法能识别出SAM未识别或识别错误的岩性。通过研究区野外查证,可知SDEM方法所得岩性分类结果更符合岩石实际分布情况。可见光谱离散能级波形匹配法具有较好的岩性分类效果,能更好地区分地物。 相似文献
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Hyperion高光谱遥感岩性识别填图 总被引：1，自引：0，他引：1
Hyperion高光谱数据在蚀变矿物填图方面已凸显其优越性,而在岩性识别方面大多仍在探讨中.通过应用Hyperion数据结合野外光谱采集,在研究区进行了岩性填图的试验研究.同时,叙述了野外采集光谱数据的过程,建立了野外实测光谱数据库,利用野外实测岩石光谱作为端元光谱进行SAM(Spectral Angle Match)光谱角岩性填图.这对于地质环境复杂地区的岩性填图工作具有一定的应用价值. 相似文献
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利用核主成分(KPCA)较强的非线性特征提取能力对Hyperion高光谱数据进行降维及光谱特征提取,将特征信息作为支持向量机(SVM)建模样本的观测数据,建立KPCA-SVM回归模型,利用该模型进行研究区岩石氧化物百分含量反演。同时,依据国际地质科学联合会提出的QAPF火成岩分类方案对区内火成岩进行了岩性划分。研究结果表明:KPCA降维后的高光谱数据反演氧化物含量的效果良好;而基于QAPF模型的火成岩划分结果也十分理想,分类结果对已有地质图进行了有效的补充。KPCA-SVM理论模型为利用高光谱遥感数据进行岩性分类提供了一种快速可行的方法。 相似文献
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植被覆盖区卫星高光谱遥感岩性分类 总被引：1，自引：0，他引：1
植被高覆盖区岩石和土壤在遥感图像上表现为弱信息、小目标,如何利用卫星高光谱遥感提取岩性弱信息是目前遥感地质应用中的最大挑战之一。以黑龙江呼玛地区为例,选择美国EO-1卫星Hyperion高光谱数据。由于植被与下伏岩石-土壤的光谱混合,分别计算研究区含土壤因子和不含土壤因子的植被指数,并对两类不同的植被指数进行主成分分析,以此分离植被和岩石-土壤组分。在含土壤因子植被指数主成分分析的二维组分散点图上,明显区分出背景植被与异常岩石-土壤组分,证实了植被与岩石-土壤组分经主成分分析分离的效果。同时在不添加土壤因子植被指数的分析中,明显区分出植被覆盖信息。通过对实验区典型岩石进行野外光谱测试,然后对光谱进行连续统去除处理,将其作为参考光谱,与分离后的岩石-土壤光谱进行光谱特征拟合（SFF）,从而成功地识别出研究区内不同岩石类型,特别是玄武岩、流纹岩、砂砾岩、安山质凝灰岩、大理岩和石英片岩识别效果较好。根据研究区内不同岩石地层单元内岩石组合特征,通过分离后的组分合成图像,成功地实现了岩性分类。与已知地质图叠加,证实通过卫星高光谱数据提取的不同岩石类型颜色边界与地质图岩性界线吻合较好。结果表明：通过植被与岩石-土壤光谱组分分离,结合高光谱遥感的光谱特征拟合,能够识别不同的岩石类型,实现植被覆盖区岩性分类。 相似文献
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ASTER数据包含有从太空测量来自地球表面的多光谱热红外辐射数据。通过对JHU光谱库中岩石矿物热红外光谱特征的分析,以及对ASTER影像TIR波段光谱特征进行分析,利用比值法和光谱角制图法SAM相结合进行石英、砂岩和硅酸盐岩类等造岩矿物识别并在西藏冈底斯东段泽当矿田应用,最后将遥感解译分析结果与该区已的地质资料进行比较。结果表明,ASTER影像TIR波段基本上能识别硅酸盐类造岩矿物,并且对于岩性识别具有很大的应用潜力。 相似文献
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高光谱遥感数据具有波段多、数据量大、处理复杂等特点, 基于GPU的高性能计算在遥感领域得到了快速发展, 为高光谱数据的快速处理提供了硬件和技术条件。采用GPU对高光谱遥感数据常用的SAM、PPI等处理算法进行应用实验, 验证基于GPU的高光谱遥感数据快速处理技术。实验采用新疆东天山地区的一景星载Hyperion数据, 利用支持IDL开发语言的GPULib、CUDA运行时API库进行算法效率的验证, 结果表明, 基于GPU的高光谱数据处理效率比常规的多核CPU主机处理效率有较大提升, 具有一定的应用推广价值。 相似文献
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基于改进的SVM技术和高光谱遥感的标准矿物定量计算 总被引：2，自引：0，他引：2
基于支持向量机(SVM)统计理论,并对其从核函数构造方面进行改进,通过主成分分析、包络线去除、光谱导数变换等对原始Hyperion高光谱数据进行降维、变换与特征提取,分析比较了这些变换后不同的回归效果,并将其应用在内蒙古霍林郭勒地区岩石中氧化物质量分数的反演中。同时,鉴于某些重要矿物本身并没有明显的特征光谱曲线,提出一种新的矿物定量方法。首先,基于高光谱遥感数据,利用改进的SVM回归技术反演矿物中的化学成分,然后通过标准矿物计算（CIPW）推导岩石中标准矿物的质量分数。研究结果表明：基于改进核函数后的SVM回归精度有所提高,其中导数变换后的反演精度达74.87%,比原始光谱反演精度提高了4.11%。CIPW应用于高光谱遥感地质填图效果良好,为岩性鉴定和评价提供了科学依据。 相似文献
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由于传统的岩性分类方法受岩石辐射干扰因素大, 存在"同物异谱"以及"同谱异物"现象, 岩性分类精度低, 所以在深入分析岩石矿物光谱特征基础上, 以西昆仑成矿带地区的二长花岗岩、石英正长岩以及正长岩为研究对象, 基于这3种岩性的实测光谱数据以及先进星载热发射和反射辐射仪(advanced spaceborne theemal emission and reflection radiometer, ASTER)影像数据的波段设置特征, 建立了RI和SI两种光谱指数.利用所建立的RI以及SI光谱指数对ASTER遥感数据进行岩性分类.结果显示, RI和SI两种光谱指数法在提取二长花岗岩时精度达到70%以上, 石英正长岩精度为80%左右, 与最大似然法得到的分类结果相比, 这两种岩性的分类精度明显提高了. 相似文献
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Performance of image classification on hyperspectral imagery for lithological mapping 总被引：1，自引：0，他引：1
Nisha Rani Venkata Ravibabu Mandla Tejpal Singh 《Journal of the Geological Society of India》2016,88(4):440-448
SVM and SAM classifiers for the lithological mapping using Hyperion data in parts of Gadag schist belt of western Dharwar craton, Karnataka, India were used. The main objective of the present study is to assess and compare the potential use of Hyperion data set for lithological mapping. Accuracy assessment of the derived thematic maps was based on the analysis of the confusion matrix statistics computed for each classification map. For consistency, the same set of validation points were used in evaluating the accuracy of the lithological thematic maps produced. On the basis of the accuracy assessment results, it appears that SVM generally outperformed the SAM classifier in both OA accuracy and individual classes’ accuracies. OA accuracy and Kc for SVM is 96.93% and 0.9655, whereas for SAM it is 74.02% and 0.7085 respectively. SVM classification is the best in describing the spatial distribution and the cover density of each lithology, as was also indicated from the statistics of the individual class results. The individual class accuracy were also analyzed for the SVM and the result show that PA ranges from 87% to 100% and UA ranges from 91% to 100%, whereas for SAM ranges from 15% to 95%, and from 31% to 100% respectively. The SVM method could effectively classify and improve on the existing geological map for the Gadag schist belt (GSB) using hyperspectral data. The results could be validated through field visits. Therefore, it is concluded that hyperspectral remote sensing data can be efficiently used to improve existing maps, especially in areas where same rock types show variable degree of alteration over smaller spatial scales. 相似文献
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Hyperspectral satellite data analysis for pure pixels extraction and evaluation of advanced classifier algorithms for LULC classification 总被引：1，自引：0，他引：1
Gopal Krishna Rabi N. Sahoo Sanatan Pradhan Tauqueer Ahmad Prachi M. Sahoo 《Earth Science Informatics》2018,11(2):159-170
The study was carried out for Indian capital city Delhi using Hyperion sensor onboard EO-1 satellite of NASA. After MODTRAN-4 based atmospheric correction, MNF, PPI and n-D visualizer were applied and endmembers of 11 LCLU classes were derived which were employed in classification of LULC. To incur better classification accuracy, a comparative study was also carried out to evaluate the potential of three classifier algorithms namely Random Forest (RF), Support Vector Machines (SVM) and Spectral Angle Mapper (SAM). The results of this study reemphasize the utility of satellite borne hyperspectral data to extract endmembers and also to delineate the potential of random forest as expert classifier to assess land cover with higher classification accuracy that outperformed the SVM by 19% and SAM by 27% in overall accuracy. This research work contributes positively to the issue of land cover classification through exploration of hyperspectral endmembers. The comparison of classification algorithms’ performance is valuable for decision makers to choose better classifier for more accurate information extraction. 相似文献
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为深入研究和探讨高分五号(GF-5)航天高光谱遥感技术在铀矿地质找矿中的应用效果和潜力,基于龙首山成矿带航天高光谱数据,开展高光谱数据处理和蚀变信息提取工作,创新实现了GF-5高光谱波段修复,通过构建标准光谱库和诊断光谱,运用MNF算法、PPI算法,结合SAM光谱角填图技术,完成蚀变矿物端元提取和光谱匹配,实现研究区钠长石、方解石、石英、绿泥石、赤铁矿和高岭土蚀变矿物的提取,综合区域铀矿成矿地质背景,通过开展地面波谱测量和野外调查,在验证蚀变准确度的基础上,剖析航天高光谱蚀变信息和成矿规律,构建了区域找矿定位模型,圈定找矿预测区3处,取得了较好的找矿效果,为国产GF-5高光谱遥感在地质找矿中的应用提供了参考。 相似文献
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Applied in Djebel Meni (Northwestern of Algeria), this research highlights the results obtained from the supervised classification using the Spectral Angle Mapper (SAM) algorithm, through introducing the spectral signatures of illite, kaolinite, and montmorillonite, via Jet Propulsion Laboratory (JPL) spectral library. These results were compared to the ones of the SAM classification, which use spectral signatures obtained by the Sequential Maximum Angle Convex Cone (SMACC) endmembers extraction algorithm. This implies the ability to detect and identify any object present on the Earth’s surface, whether its nature is mineral, vegetal, or human made, from hyperspectral imaging. By extracting the spectral signatures with the SMACC algorithm and matching them to the current signatures of JPL spectral library, comparing spectral signatures with another is not an easy task. Indeed, for a better comparison and a more appropriate interpretation in the use of the SAM classification, the results obtained were very relatively convincing because, regarding very strong similarities. It appears also that the signatures extracted with SMACC occupy the same areas as those of the JPL spectral library. This method of detection and identification of any present object on the Earth’s surface is rather conclusive. 相似文献
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Narayan Kayet Khanindra Pathak Abhisek Chakrabarty Satiprasad Sahoo 《Environmental Earth Sciences》2018,77(8):308
The prime contribution of this assignment was to examine the hyperspectral remote sensing, based on iron ore minerals identification using spectral angle mapper (SAM) technique. Correlation analyses between field iron contents and environmental variables (soil, water, and vegetation) have been performed. Spectral feature fitting (SFF) and multi-range spectral feature fitting (MRSFF) methods were used for accuracy assessment in extracting iron ore minerals from Hyperion EO-1 data. Spectral inspections as a reference were used in SAM technique for image classification for iron ore minerals: Hematite (24.26%), Goethite (32.98%) and Desert (42.76). Iron ore minerals classification is justified by the United States Geological Survey (USGS) spectral library and field sample points. The regression analysis of USGS and Hyperion reflectance spectra has shown the moderate positive correlation. The regression analyses between iron ore contents and environmental parameters (soil, water, and vegetation) have shown the moderate negative correlation. The examination was significantly effectual in extracting iron ore minerals: Hematite (SFF RMSE?≤?0.51 MRSFF RMSE?≤?0.48), Goethite (SFF RMSE?≤?0.047 MRSFF RMSE?≤?0.438) and Desert (SFF RMSE?≤?0.63 and MRSFF RMSE?≤?0.50); and the MRSFF RMSE histograms indicate the above result likened to a conventional SFF RMSE. MRSFF RMS error result is best because multiple absorption features typically characterize spectral signatures. This analysis demonstrates the potential applicability of the methodology for iron minerals identification framework and iron minerals impact on environmental parameters. 相似文献
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遥感技术广泛应用于地质基础调查、矿产资源勘探、环境评估和地质灾害调查等方面。它已从多光谱发展到高光谱阶段,Landsat- 8是目前最具有代表性和最常用的多光谱数据,ASTER具有高的分辨率和多波段特征,资源一号02D(ZY1- 02D)卫星是我国2019年发射的高光谱业务卫星。为了更好地了解多源遥感数据在岩矿识别中的作用,在新疆东天山卡拉麦里地区进行了相关研究。结果表明：Landsat- 8 OLI的PCA变换结果清晰识别了研究区不同的岩性和地层;使用Landsat- 8 OLI、ASTER和ZY1- 02D高光谱数据,分别采取不同的图像端元提取方法,在进行光谱分析的基础上,利用光谱角填图（SAM）即可得到研究区的主要矿物分类图件。通过野外验证,应用GIS技术进行集成和分析,修正相关图件后,便得到了精准的矿物分类综合图。研究表明：多源遥感数据的集成在岩矿识别方面效果良好、前景巨大。 相似文献