对所获取的2008年冬季的辽东湾西岸海域含有海冰的Hyperion高光谱图像进行了大气校正,得到了反射率图像。用ISODATA(Iterative Self-Organizing Data Analysis Technique)聚类分析方法对反射率图像进行计算机自动分类,并结合实测的同时...对所获取的2008年冬季的辽东湾西岸海域含有海冰的Hyperion高光谱图像进行了大气校正,得到了反射率图像。用ISODATA(Iterative Self-Organizing Data Analysis Technique)聚类分析方法对反射率图像进行计算机自动分类,并结合实测的同时期的海冰反射率光谱确定了不同海冰类型的分布范围。根据不同类型海冰的厚度特征,得到了海冰厚度分级分布图和海冰厚度图。结果表明,Hyperion图像可以区分光谱有区别的冰型,无法区分浮冰和固定冰,可以更清晰地显示出海冰的光谱反射率,与实测光谱曲线更加相似,优于MODIS多光谱图像。同时,用主成分分析方法对海冰Hyperion图像进行了分析。海冰Hyperion图像中,各个波段之间的相关系数都较大,光谱维信息冗余度较大,其中30波段贡献率最高。展开更多
Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information...Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant(SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set's reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis(PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting(SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.展开更多
Information on Earth’s land surface cover is commonly obtained through digital image analysis of data acquired from remote sensing sensors.In this study,we evaluated the use of diverse classification techniques in di...Information on Earth’s land surface cover is commonly obtained through digital image analysis of data acquired from remote sensing sensors.In this study,we evaluated the use of diverse classification techniques in discriminating land use/cover types in a typical Mediterranean setting using Hyperion imagery.For this purpose,the spectral angle mapper(SAM),the object-based and the non-linear spectral unmixing based on artificial neural networks(ANNs)techniques were applied.A further objective had been to investigate the effect of two approaches for training sites selection in the SAM classification,namely of the pixel purity index(PPI)and of the direct selection of training points from the Hyperion imagery assisted by a QuickBird imagery and field-based training sites.Objectbased classification outperformed the other techniques with an overall accuracy of 83%.Sub-pixel classification based on the ANN showed an overall accuracy of 52%,very close to that of SAM(48%).SAM applied using the training sites selected directly from the Hyperion imagery supported by the QuickBird image and the field visits returned an increase accuracy by 16%.Yet,all techniques appeared to suffer from the relatively low spatial resolution of the Hyperion imagery,which affected the spectral separation among the land use/cover classes.展开更多
文摘对所获取的2008年冬季的辽东湾西岸海域含有海冰的Hyperion高光谱图像进行了大气校正,得到了反射率图像。用ISODATA(Iterative Self-Organizing Data Analysis Technique)聚类分析方法对反射率图像进行计算机自动分类,并结合实测的同时期的海冰反射率光谱确定了不同海冰类型的分布范围。根据不同类型海冰的厚度特征,得到了海冰厚度分级分布图和海冰厚度图。结果表明,Hyperion图像可以区分光谱有区别的冰型,无法区分浮冰和固定冰,可以更清晰地显示出海冰的光谱反射率,与实测光谱曲线更加相似,优于MODIS多光谱图像。同时,用主成分分析方法对海冰Hyperion图像进行了分析。海冰Hyperion图像中,各个波段之间的相关系数都较大,光谱维信息冗余度较大,其中30波段贡献率最高。
基金Under the auspices of National Science and Technology Major Project of China(No.04-Y20A35-9001-15/17)the Program for JLU Science and Technology Innovative Research Team(No.JLUSTIRT,2017TD-26)the Changbai Mountain Scholars Program,Jilin Province,China
文摘Remote sensing data have been widely applied to extract minerals in geologic exploration, however, in areas covered by vegetation, extracted mineral information has mostly been small targets bearing little information. In this paper, we present a new method for mineral extraction aimed at solving the difficulty of mineral identification in vegetation covered areas. The method selected six sets of spectral difference coupling between soil and plant(SVSCD). These sets have the same vegetation spectra reflectance and a maximum different reflectance of soil and mineral spectra from Hyperion image based on spectral reflectance characteristics of measured spectra. The central wavelengths of the six, selected band pairs were 2314 and 701 nm, 1699 and 721 nm, 1336 and 742 nm, 2203 and 681 nm, 2183 and 671 nm, and 2072 and 548 nm. Each data set's reflectance was used to calculate the difference value. After band difference calculation, vegetation information was suppressed and mineral abnormal information was enhanced compared to the scatter plot of original band. Six spectral difference couplings, after vegetation inhibition, were arranged in a new data set that requires two components that have the largest eigenvalue difference from principal component analysis(PCA). The spatial geometric structure features of PC1 and PC2 was used to identify altered minerals by spectral feature fitting(SFF). The collecting rocks from the 10 points that were selected in the concentration of mineral extraction were analyzed under a high-resolution microscope to identify metal minerals and nonmetallic minerals. Results indicated that the extracted minerals were well matched with the verified samples, especially with the sample 2, 4, 5 and 8. It demonstrated that the method can effectively detect altered minerals in vegetation covered area in Hyperion image.
文摘Information on Earth’s land surface cover is commonly obtained through digital image analysis of data acquired from remote sensing sensors.In this study,we evaluated the use of diverse classification techniques in discriminating land use/cover types in a typical Mediterranean setting using Hyperion imagery.For this purpose,the spectral angle mapper(SAM),the object-based and the non-linear spectral unmixing based on artificial neural networks(ANNs)techniques were applied.A further objective had been to investigate the effect of two approaches for training sites selection in the SAM classification,namely of the pixel purity index(PPI)and of the direct selection of training points from the Hyperion imagery assisted by a QuickBird imagery and field-based training sites.Objectbased classification outperformed the other techniques with an overall accuracy of 83%.Sub-pixel classification based on the ANN showed an overall accuracy of 52%,very close to that of SAM(48%).SAM applied using the training sites selected directly from the Hyperion imagery supported by the QuickBird image and the field visits returned an increase accuracy by 16%.Yet,all techniques appeared to suffer from the relatively low spatial resolution of the Hyperion imagery,which affected the spectral separation among the land use/cover classes.
