Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still...Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 - 2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 - 2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey’s HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey’s HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop while three spectral zones were the best to identify rice crop (350:713, 1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was considered in the process. These results will be used in machine learning process to improve the performance of the existing remote sensing software’s to isolate the different crops in intensive cultivated lands. The study was carried out in Damietta governorate of Egypt.展开更多
Hydrocarbon micro-seepage can cause oxidation reduction reactions and produce altered minerals in surface sediments and soft. The typical altered minerals mapping by their diagnostic spectral features on hyper-spectra...Hydrocarbon micro-seepage can cause oxidation reduction reactions and produce altered minerals in surface sediments and soft. The typical altered minerals mapping by their diagnostic spectral features on hyper-spectral images is an important tool for the petroleum exploration industry. In this study, the airborne hyper-spectral data were used to investigate the altered minerals induced by hydrocarbon micro-seepages by spectral feature fitting （SFF） in the loess coverage area of Xifeng Oflfield. The results re- veal that the distribution region of the altered minerals induced by hydrocarbon micro-seepage is larger than the known oilfield exploration area. The potential hydrocarbon micro-seepage region was also re- vealed by the distribution of altered minerals besides the known hydrocarbon area. A fast index was pro- posed by the absorption depths of clay and carbonate minerals for assessment of hydrocarbon micro- seepage. And it gave much clearer boundaries for the hydrocarbon micro-seepage in the loess coverage area than those by the altered mineral mapping. In addition, some field samples were analyzed by X-ray diffrac- tion （XRD） and atomic absorption spectrophotometer to validate the results. Within the extents of hydro- carbon micro-seepage, there are lower contents of ferric iron and higher contents of carbonate minerals in these samples. Therefore, it is satisfactory to have the airborne hyper-spectral data to outline the extents of hydrocarbon micro-seepage for further hydrocarbon exploration in the loess coverage area.展开更多
Hyper-spectral imaging spectrometer has high spatial and spectral resolution. Its radiometric calibration needs the knowledge of the sources used with high spectral resolution. In order to satisfy the requirement of s...Hyper-spectral imaging spectrometer has high spatial and spectral resolution. Its radiometric calibration needs the knowledge of the sources used with high spectral resolution. In order to satisfy the requirement of source, an on-orbit radiometric calibration method is designed in this paper. This chain is based on the spectral inversion accuracy of the calibration light source. We compile the genetic algorithm progress which is used to optimize the channel design of the transfer radiometer and consider the degradation of the halogen lamp, thus realizing the high accuracy inversion of spectral curve in the whole working time. The experimental results show the average root mean squared error is 0.396%, the maximum root mean squared error is 0.448%, and the relative errors at all wavelengths are within 1% in the spectral range from 500 nm to 900 nm during 100 h operating time. The design lays a foundation for the high accuracy calibration of imaging spectrometer.展开更多
A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial l...A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.展开更多
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
森林生态系统碳循环是目前全球变化研究中的一个热点问题,叶面积指数(leaf area index,LAI)是森林生态系统碳循环模型中的一个重要的输入参数。准确地获取LAI的空间分布对提高碳循环模型的模拟精度具有重要意义。高光谱影像反演LAI比多...森林生态系统碳循环是目前全球变化研究中的一个热点问题,叶面积指数(leaf area index,LAI)是森林生态系统碳循环模型中的一个重要的输入参数。准确地获取LAI的空间分布对提高碳循环模型的模拟精度具有重要意义。高光谱影像反演LAI比多光谱影像具有明显的优势。以福建永安重点林区为研究区,以EO—1 Hyperion高光谱影像为数据源开展森林LAI反演模型研究,在对不同类型植被指数以及不同近红外/红波段组合构建的植被指数与实测LAI相关性做综合分析比较的基础上,最终建立研究区高精度LAI反演模型。该研究对于提高福建乃至全国森林LAI反演精度和碳循环的模拟能力、增强国际竞争力具有重要的意义。展开更多
由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节。文章介绍了EO-1hyperion高光谱数据的特点,以及用FLAASH(Fast Lin...由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节。文章介绍了EO-1hyperion高光谱数据的特点,以及用FLAASH(Fast Line of Sight Atmospheric Analysis of Spectral Hyper-cubes)模块对新疆地区Hyperion高光谱遥感影像进行大气校正,并对处理结果进行评价,结果表明FLAASH模块大气纠正效果良好。展开更多
文摘Crop discrimination through satellite imagery is still problematic. Accuracy of crop classification for high spatial resolution satellite imagery in the intensively cultivated lands of the Egyptian Nile delta is still low. Therefore, the main objective of this research is to determine the optimal hyperspectral wavebands in the spectral range of (400 - 2500 nm) to discriminate between two winter crops (Wheat and Clover) and two summer crops (Maize and Rice). This is considered as a first step to improve crop classification through satellite imagery in the intensively cultivated areas in Egypt. Hyperspectral ground measurements of ASD field Spec3 spectroradiometer was used to monitor the spectral reflectance profile during the period of the maximum growth stage of the four crops. 1-nm-wide was aggregated to 10-nm-wide bandwidths. After accounting for atmospheric windows and/or areas of significant noise, a total of 2150 narrow bands in 400 - 2500 nm were used in the analysis. Spectral reflectance was divided into six spectral zones: blue, green, red, near-infrared, shortwave infrared-I and shortwave infrared-II. One Way ANOVA and Tukey’s HSD post hoc analysis was performed to choose the optimal spectral zone that could be used to differentiate the different crops. Then, linear regression discrimination (LDA) was used to identify the specific optimal wavebands in the spectral zones in which each crop could be spectrally identified. The results of Tukey’s HSD showed that blue, NIR, SWIR-1 and SWIR-2 spectral zones are more sufficient in the discrimination between wheat and clover than green and red spectral zones. At the same time, all spectral zones were quite sufficient to discriminate between rice and maize. The results of (LDA) showed that the wavelength zone (727:1299 nm) was the optimal to identify clover crop while three zones (350:712, 1451:1562, 1951:2349 nm) could be used to identify wheat crop. The spectral zone (730:1299 nm) was the optimal to identify maize crop while three spectral zones were the best to identify rice crop (350:713, 1451:1532, 1951:2349 nm). An average of thirty measurements for each crop was considered in the process. These results will be used in machine learning process to improve the performance of the existing remote sensing software’s to isolate the different crops in intensive cultivated lands. The study was carried out in Damietta governorate of Egypt.
基金supported by the National High Technology Research and Development Program of China(No.2012AA12A308)China Geological Surveys(No.1212011087112)
文摘Hydrocarbon micro-seepage can cause oxidation reduction reactions and produce altered minerals in surface sediments and soft. The typical altered minerals mapping by their diagnostic spectral features on hyper-spectral images is an important tool for the petroleum exploration industry. In this study, the airborne hyper-spectral data were used to investigate the altered minerals induced by hydrocarbon micro-seepages by spectral feature fitting （SFF） in the loess coverage area of Xifeng Oflfield. The results re- veal that the distribution region of the altered minerals induced by hydrocarbon micro-seepage is larger than the known oilfield exploration area. The potential hydrocarbon micro-seepage region was also re- vealed by the distribution of altered minerals besides the known hydrocarbon area. A fast index was pro- posed by the absorption depths of clay and carbonate minerals for assessment of hydrocarbon micro- seepage. And it gave much clearer boundaries for the hydrocarbon micro-seepage in the loess coverage area than those by the altered mineral mapping. In addition, some field samples were analyzed by X-ray diffrac- tion （XRD） and atomic absorption spectrophotometer to validate the results. Within the extents of hydro- carbon micro-seepage, there are lower contents of ferric iron and higher contents of carbonate minerals in these samples. Therefore, it is satisfactory to have the airborne hyper-spectral data to outline the extents of hydrocarbon micro-seepage for further hydrocarbon exploration in the loess coverage area.
基金supported by the National Natural Science Foundation of China(No.41474161)the National High Technology Research and Development Program of China(No.2015AA123703)
文摘Hyper-spectral imaging spectrometer has high spatial and spectral resolution. Its radiometric calibration needs the knowledge of the sources used with high spectral resolution. In order to satisfy the requirement of source, an on-orbit radiometric calibration method is designed in this paper. This chain is based on the spectral inversion accuracy of the calibration light source. We compile the genetic algorithm progress which is used to optimize the channel design of the transfer radiometer and consider the degradation of the halogen lamp, thus realizing the high accuracy inversion of spectral curve in the whole working time. The experimental results show the average root mean squared error is 0.396%, the maximum root mean squared error is 0.448%, and the relative errors at all wavelengths are within 1% in the spectral range from 500 nm to 900 nm during 100 h operating time. The design lays a foundation for the high accuracy calibration of imaging spectrometer.
基金National Key Research and Development Program of China(No.2016YFF0103604)National Natural Science Foundations of China(Nos.61171165,11431015,61571230)+1 种基金National Scientific Equipment Developing Project of China(No.2012YQ050250)Natural Science Foundation of Jiangsu Province,China(No.BK20161500)
文摘A comprehensive assessment of the spatial.aware mpervised learning algorithms for hyper.spectral image （HSI） classification was presented. For this purpose, standard support vector machines （ SVMs ）, mudttnomial logistic regression （ MLR ） and sparse representation （SR） based supervised learning algorithm were compared both theoretically and experimentally. Performance of the discussed techniques was evaluated in terms of overall accuracy, average accuracy, kappa statistic coefficients, and sparsity of the solutions. Execution time, the computational burden, and the capability of the methods were investigated by using probabilistie analysis. For validating the accuracy a classical benchmark AVIRIS Indian pines data set was used. Experiments show that integrating spectral.spatial context can further improve the accuracy, reduce the misclassltication error although the cost of computational time will be increased.
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。
文摘森林生态系统碳循环是目前全球变化研究中的一个热点问题,叶面积指数(leaf area index,LAI)是森林生态系统碳循环模型中的一个重要的输入参数。准确地获取LAI的空间分布对提高碳循环模型的模拟精度具有重要意义。高光谱影像反演LAI比多光谱影像具有明显的优势。以福建永安重点林区为研究区,以EO—1 Hyperion高光谱影像为数据源开展森林LAI反演模型研究,在对不同类型植被指数以及不同近红外/红波段组合构建的植被指数与实测LAI相关性做综合分析比较的基础上,最终建立研究区高精度LAI反演模型。该研究对于提高福建乃至全国森林LAI反演精度和碳循环的模拟能力、增强国际竞争力具有重要的意义。
文摘由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节。文章介绍了EO-1hyperion高光谱数据的特点,以及用FLAASH(Fast Line of Sight Atmospheric Analysis of Spectral Hyper-cubes)模块对新疆地区Hyperion高光谱遥感影像进行大气校正,并对处理结果进行评价,结果表明FLAASH模块大气纠正效果良好。
