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
Haze is mainly caused by the suspended particulate matters in the air,of which the particulate matters pollution harms leaf vegetables.In this paper,oilseed rapes at four different growing periods were investigated in...Haze is mainly caused by the suspended particulate matters in the air,of which the particulate matters pollution harms leaf vegetables.In this paper,oilseed rapes at four different growing periods were investigated in a simulated particulate pollution environment.In combination of hyper-spectral technology and micro examination,the response of hyper-spectral characteristics of the leaf to particulate matters was investigated in-depth.The hyperspectral,chlorophyll content,net photosynthetic rate and stomatal conductance of leaf were obtained.The deposition and adsorption of particulate matters on the leaf were observed by Environmental Scanning Electron Microscope(ESEM).Normalized difference vegetation index(NDVI),modified red edge normalized(mNDVI705)and modified red edge simple ratio index(mSR705)were selected as characteristic parameters and the range of 510 nm~620 nm as the sensitive band.16 methods were used to establish the physiological information inversion model.The main results were as follows:Under the influence of particulate matters,the spectral reflectance decreased as a whole.With the increase of leaf age,the phenomenon of blue shift aggravated.The amplitude of yellow and blue edge decreased with overall decreasing vegetation indices.The furrows and irregular band protrusions in leaves were favorable for keeping particulate matters.With longer affecting time and more deposition of particle matters on the leaf,the stomatal opening became smaller.After comparing,principal component regression(PCR)+multiple scatter correction(MSC)+second derivative(SD)+Savitzky-Golay smooth(SG),and partial least square(PLS)+multiple scatter correction(MSC)+first derivative(FD)+Savitzky-Golay smooth(SG)were determined the best method to establish the inversion model of chlorophyll content and net photosynthetic rate respectively.This study may bring novel ideas for the diagnosis and analysis of the physiological response of leaf vegetables under particulate matters pollution using hyper-spectral technology.展开更多
High-resolution hyper-spectral image (HHR) provides both detailed structural and spectral information for urban study. However, due to the inherent correlation between spectral bands and within-class variability in th...High-resolution hyper-spectral image (HHR) provides both detailed structural and spectral information for urban study. However, due to the inherent correlation between spectral bands and within-class variability in the data, the data processing of HHR is a challenging work. In this paper, based on spectral mixture analysis theory, a new stack of parts description features were extracted, and then incorporated with a stack of morphology based spatial features. Partially supervised constrained energy minimization (CEM) and unsupervised nonnegative matrix factorization (NMF) were used to extract the part-features. The joint features were then integrated by SVM classifier. The advantages of this method are the representation of physical composition of the urban area by the parts-features and the show of multi-scale structure information by morphology profiles. Experiments with an airborne hyper-spectral data flightline over the Washington DC Mall were performed, and the performance of the proposed algorithm was evaluated in comparison with well-known nonparametric weighted feature extraction (NWFE) and feature selection method. The results shown that the proposed features-joint scheme consistently outperforms the traditional methods, and so can provide an effective option for processing HHR data in urban area.展开更多
A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition （EMD）, from an artificial red tide experiment in the East C...A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition （EMD）, from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general pictre data, with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile, the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence, with which a red tide can be forecast.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
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
The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects classification.Despite the classification value of hyper-spectral imaging technology within vario...The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects classification.Despite the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor environment.This research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral images.It involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition firstly.Then maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects classification.The performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.展开更多
An algorithm for retrieving the surface pressure from oxygen A-band measurements in the future Chinese CO2satellite(CarbonSpec/TanSat)was developed.The ful physical radiative transfer model,vector radiative transfe mo...An algorithm for retrieving the surface pressure from oxygen A-band measurements in the future Chinese CO2satellite(CarbonSpec/TanSat)was developed.The ful physical radiative transfer model,vector radiative transfe model based on successive order of scattering,which i based on the successive order of scattering approach,wa used to simulate the measurements of CarbonSpec/TanSat as well as the kernel matrix in the inversion algorithm,and then the surface pressure and other related atmospheric parameters such as aerosol optical depth(AOD),surface albedo,and temperature were derived through optima estimation theory.Sensitivities of the algorithm to surface albedo,solar zenith angle(SZA),viewing zenith angle(VZA),aerosol type,and AOD were investigated,and the results showed that the absolute error of retrieved surface pressure increases with decreasing surface albedo o increasing SZA and VZA.An accuracy of\4 hPa ove bright surfaces(surface albedo C0.15)could be derived fo various SZAs and viewing geometries.Moreover,the algorithm can simultaneously retrieve the surface albedo AOD,and its vertical distribution indicated by scale展开更多
The Hyperspectral Infrared Atmospheric Sounder-II(HIRAS-II)onboard China’s FungYun(FY)-3F meteorological satellite was launched in August 2023.This study presents the first attempt to retrieve the global carbon monox...The Hyperspectral Infrared Atmospheric Sounder-II(HIRAS-II)onboard China’s FungYun(FY)-3F meteorological satellite was launched in August 2023.This study presents the first attempt to retrieve the global carbon monoxide(CO)column from HIRAS-II/FY-3F spectra based on a newly established full-physics algorithm.The CO global columns derived from the HIRAS-II/FY-3F satellite are compared to measurements from the Infrared Atmospheric Sounding Interferometer(IASI)onboard Europe’s MetopB satellite,as both satellites have the same spectral range with a similar overpass time.The correlation coefficient between the IASI/Metop-B and HIRAS-II/FY-3F CO retrievals is about 0.8.The HIRAS-II/FY-3F satellite can capture well the regions with high CO values,e.g.,Africa,North America,and East Asia.The relative difference in the CO global column between HIRAS-II and IASI is 1.2±13.7(1)%,which is within their combined retrieval uncertainty.The CO plumes from the fire emissions in North America between 18 and 23 July 2024 were observed by the HIRAS-II/FY-3F satellite and consistent with the CAMS(Copernicus Atmosphere Monitoring Service)model simulations.Our results show that the HIRAS-II/FY-3F spectra are of good enough quality to provide quantitative observations of global CO column remote sensing observations.展开更多
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This work was funded under the auspices of the National Natural Science Foundation for Young Scientists Fund(31801259)the National Natural Science Foundation for Young Scientists Fund(32001418)the Science and Technology Development Project of Jilin Province(20200402015NC).
文摘Haze is mainly caused by the suspended particulate matters in the air,of which the particulate matters pollution harms leaf vegetables.In this paper,oilseed rapes at four different growing periods were investigated in a simulated particulate pollution environment.In combination of hyper-spectral technology and micro examination,the response of hyper-spectral characteristics of the leaf to particulate matters was investigated in-depth.The hyperspectral,chlorophyll content,net photosynthetic rate and stomatal conductance of leaf were obtained.The deposition and adsorption of particulate matters on the leaf were observed by Environmental Scanning Electron Microscope(ESEM).Normalized difference vegetation index(NDVI),modified red edge normalized(mNDVI705)and modified red edge simple ratio index(mSR705)were selected as characteristic parameters and the range of 510 nm~620 nm as the sensitive band.16 methods were used to establish the physiological information inversion model.The main results were as follows:Under the influence of particulate matters,the spectral reflectance decreased as a whole.With the increase of leaf age,the phenomenon of blue shift aggravated.The amplitude of yellow and blue edge decreased with overall decreasing vegetation indices.The furrows and irregular band protrusions in leaves were favorable for keeping particulate matters.With longer affecting time and more deposition of particle matters on the leaf,the stomatal opening became smaller.After comparing,principal component regression(PCR)+multiple scatter correction(MSC)+second derivative(SD)+Savitzky-Golay smooth(SG),and partial least square(PLS)+multiple scatter correction(MSC)+first derivative(FD)+Savitzky-Golay smooth(SG)were determined the best method to establish the inversion model of chlorophyll content and net photosynthetic rate respectively.This study may bring novel ideas for the diagnosis and analysis of the physiological response of leaf vegetables under particulate matters pollution using hyper-spectral technology.
基金Supported by the Major State Basic Research Development Program(973Program)of China(No.2009CB723905)the National High TechnologyResearch and Development Program(863Program)of China(No.2009AA12Z114)the National Natural Science Foundation of China(Nos.40930532,40901213,40771139)
文摘High-resolution hyper-spectral image (HHR) provides both detailed structural and spectral information for urban study. However, due to the inherent correlation between spectral bands and within-class variability in the data, the data processing of HHR is a challenging work. In this paper, based on spectral mixture analysis theory, a new stack of parts description features were extracted, and then incorporated with a stack of morphology based spatial features. Partially supervised constrained energy minimization (CEM) and unsupervised nonnegative matrix factorization (NMF) were used to extract the part-features. The joint features were then integrated by SVM classifier. The advantages of this method are the representation of physical composition of the urban area by the parts-features and the show of multi-scale structure information by morphology profiles. Experiments with an airborne hyper-spectral data flightline over the Washington DC Mall were performed, and the performance of the proposed algorithm was evaluated in comparison with well-known nonparametric weighted feature extraction (NWFE) and feature selection method. The results shown that the proposed features-joint scheme consistently outperforms the traditional methods, and so can provide an effective option for processing HHR data in urban area.
基金Shandong Natural Science Fund (No.Y2007G32)the Doctoral Fund of Qingdao University of Science & Technology (No.0022143).
文摘A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition （EMD）, from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general pictre data, with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile, the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence, with which a red tide can be forecast.
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.
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supported by the Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing under Grant No.2016CP01,Xi’an University of Technology,Xi’an Science and Technology Plan Projects under Grant No.NC1504(2)the National Natural Science Foundation of China under Grant No.31101075+1 种基金the National High Technology Research and Development of China(863 Program)under Grant No.2013AA10230402,Natural Science Fundamental Research Plan of Shaanxi Province under Grant No.2016JM6038Fundamental Research Funds for the Central Universities,NWSUAF,China,Grant No.2452015060.
文摘The hyper-spectral image contains spectral and spatial information,which increases the ability and precision of objects classification.Despite the classification value of hyper-spectral imaging technology within various applications,users often find it difficult to effectively apply in practice because of the effect of light,temperature and wind in outdoor environment.This research presented a new classification model for outdoor farmland objects based on near-infrared(NIR)hyper-spectral images.It involves two steps including region of interest(ROI)acquisition and establishment of classifiers.A distance-based method for quantitative analysis was proposed to optimize the reference pixels in ROI acquisition firstly.Then maximum likelihood(ML)and support vector machine(SVM)were used for farmland objects classification.The performance of the proposed method showed that the total classification accuracy based on the reference pixels was over 97.5%,of which the SVM-M model could reach 99.5%.The research provided an effective method for outdoor farmland image classification.
基金supported by the Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues(XDA05040300)the National High-Tech R&D Program(2011AA12A104)of Chinathe National Natural Sience Foundation of China(41305030)
文摘An algorithm for retrieving the surface pressure from oxygen A-band measurements in the future Chinese CO2satellite(CarbonSpec/TanSat)was developed.The ful physical radiative transfer model,vector radiative transfe model based on successive order of scattering,which i based on the successive order of scattering approach,wa used to simulate the measurements of CarbonSpec/TanSat as well as the kernel matrix in the inversion algorithm,and then the surface pressure and other related atmospheric parameters such as aerosol optical depth(AOD),surface albedo,and temperature were derived through optima estimation theory.Sensitivities of the algorithm to surface albedo,solar zenith angle(SZA),viewing zenith angle(VZA),aerosol type,and AOD were investigated,and the results showed that the absolute error of retrieved surface pressure increases with decreasing surface albedo o increasing SZA and VZA.An accuracy of\4 hPa ove bright surfaces(surface albedo C0.15)could be derived fo various SZAs and viewing geometries.Moreover,the algorithm can simultaneously retrieve the surface albedo AOD,and its vertical distribution indicated by scale
基金supported by the FengYun Application Pioneering Project (Grant No. FY-APP-2022.0502)the National Natural Science Foundation of China (42205140)the State Key Laboratory of Atmospheric Environment end Extreme Meteorology (Grant No. 2024QN04)
文摘The Hyperspectral Infrared Atmospheric Sounder-II(HIRAS-II)onboard China’s FungYun(FY)-3F meteorological satellite was launched in August 2023.This study presents the first attempt to retrieve the global carbon monoxide(CO)column from HIRAS-II/FY-3F spectra based on a newly established full-physics algorithm.The CO global columns derived from the HIRAS-II/FY-3F satellite are compared to measurements from the Infrared Atmospheric Sounding Interferometer(IASI)onboard Europe’s MetopB satellite,as both satellites have the same spectral range with a similar overpass time.The correlation coefficient between the IASI/Metop-B and HIRAS-II/FY-3F CO retrievals is about 0.8.The HIRAS-II/FY-3F satellite can capture well the regions with high CO values,e.g.,Africa,North America,and East Asia.The relative difference in the CO global column between HIRAS-II and IASI is 1.2±13.7(1)%,which is within their combined retrieval uncertainty.The CO plumes from the fire emissions in North America between 18 and 23 July 2024 were observed by the HIRAS-II/FY-3F satellite and consistent with the CAMS(Copernicus Atmosphere Monitoring Service)model simulations.Our results show that the HIRAS-II/FY-3F spectra are of good enough quality to provide quantitative observations of global CO column remote sensing observations.
