Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spec...Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task.Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates（FARs） with respect to those achieved by conventional hyperspectral target detectors.展开更多
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'a...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 ex-plain 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-1;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 esti-mating 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.展开更多
Aiming at the problem of coal gangue identifcation in the current fully mechanized mining face and coal washing,this article proposed a convolution neural network(CNN)coal and rock identifcation method based on hypers...Aiming at the problem of coal gangue identifcation in the current fully mechanized mining face and coal washing,this article proposed a convolution neural network(CNN)coal and rock identifcation method based on hyperspectral data.First,coal and rock spectrum data were collected by a near-infrared spectrometer,and then four methods were used to flter 120 sets of collected data:frst-order diferential(FD),second-order diferential(SD),standard normal variable transformation(SNV),and multi-style smoothing.The coal and rock refectance spectrum data were pre-processed to enhance the intensity of spectral refectance and absorption characteristics,as well as efectively remove the spectral curve noise generated by instrument performance and environmental factors.A CNN model was constructed,and its advantages and disadvantages were judged based on the accuracy of the three parameter combinations(i.e.,the learning rate,the number of feature extraction layers,and the dropout rate)to generate the best CNN classifer for the hyperspectral data for rock recognition.The experiments show that the recognition accuracy of the one-dimensional CNN model proposed in this paper reaches 94.6%.Verifcation of the advantages and efectiveness of the method were proposed in this article.展开更多
Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive p...Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large,forested areas influenced by both the effects of bidirectional reflectance distribution function(BRDF)and cloud shadow contamination.In this study,hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry's LiDAR,CCD,and hyperspectral systems(CAF-LiCHy).After BRDF correction and cloud shadow detection processing,a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands,spectral vegetation indices,and texture information.Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data.Cloud-shaded pixels also have good spectral separability for species classification.The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions.According to the classification accuracies through field survey data at multiple spatial scales,it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.展开更多
Hyperspectral data fusion technique is the key to hyperspectral data processing in recent years. Many fusion methods have been proposed, but little research has been done to evaluate the performances of different data...Hyperspectral data fusion technique is the key to hyperspectral data processing in recent years. Many fusion methods have been proposed, but little research has been done to evaluate the performances of different data fusion methods. In order to meet the urgent need, quantitative correlation analysis(QCA) is proposed to analyse and compare the performances of different fusion methods directly from data before and after fusion. Experiment results show that the new method is effective and the results of comparison are in agreement with the results of application.展开更多
Hyperspectral images have wide applications in the fields of geology,mineral exploration,agriculture,forestry and environmental studies etc.due to their narrow band width with numerous channels.However,these images co...Hyperspectral images have wide applications in the fields of geology,mineral exploration,agriculture,forestry and environmental studies etc.due to their narrow band width with numerous channels.However,these images commonly suffer from atmospheric effects,thereby limiting their use.In such a situation,atmospheric correction becomes a necessary pre-requisite for any further processing and accurate interpretation of spectra of different surface materials/objects.In the present study,two very advance atmospheric approaches i.e.QUAC and FLAASH have been applied on the hyperspectral remote sensing imagery.The spectra of vegetation,man-made structure and different minerals from the Gadag area of Karnataka,were extracted from the raw image and also from the QUAC and FLAASH corrected images.These spectra were compared among themselves and also with the existing USGS and JHU spectral library.FLAASH is rigorous atmospheric algorithm and requires various parameters to perform but it has capability to compensate the effects of atmospheric absorption.These absorption curves in any spectra play an important role in identification of the compositions.Therefore,the presence of unwanted absorption features can lead to wrong interpretation and identification of mineral composition.FLAASH also has an advantage of spectral polishing which provides smooth spectral curves which helps in accurate identification of composition of minerals.Therefore,this study recommends that FLAASH is better than QUAC for atmospheric correction and correct interpretation and identification of composition of any object or minerals.展开更多
Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Theref...Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.展开更多
This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is prop...This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is proposed, namely minimum distance constrained nonnegative matrix factoriza- tion （MDC-NMF）. In this paper, firstly, a new regularization term, called endmember distance （ED） is considered, which is defined as the sum of the squared Euclidean distances from each end- member to their geometric center. Compared with the simplex volume, ED has better optimization properties and is conceptually intuitive. Secondly, a projected gradient （PG） scheme is adopted, and by the virtue of ED, in this scheme the optimal step size along the feasible descent direction can be calculated easily at each iteration. Thirdly, a finite step （ no more than the number of endmem- bers） terminated algorithm is used to project a point on the canonical simplex, by which the abun- dance nonnegative constraint and abundance sum-to-one constraint can be accurately satisfied in a light amount of computation. The experimental results, based on a set of synthetic data and real da- ta, demonstrate that, in the same running time, MDC-NMF outperforms several other similar meth- ods proposed recently.展开更多
Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of m...Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, Huan Jing-Hyper Spectral Imager(HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm(NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index(NSSRI) was constructed from continuum-removed reflectance(CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area(NDVI705 < 0.2). The soil adjusted salinity index(SAVI) was applied to predict the soil salt content in the vegetation-covered area(NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping(R2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.展开更多
Based on the raw data of spaceborne dispersive and interferometry imaging spectrometer,a set of quality evaluation metrics for compressed hyperspectral data is initially established in this paper.These quality evaluat...Based on the raw data of spaceborne dispersive and interferometry imaging spectrometer,a set of quality evaluation metrics for compressed hyperspectral data is initially established in this paper.These quality evaluation metrics,which consist of four aspects including compression statistical distortion,sensor performance evaluation,data application performance and image quality,are suited to the comprehensive and systematical analysis of the impact of lossy compression in spaceborne hyperspectral remote sensing data quality.Furthermore,the evaluation results would be helpful to the selection and optimization of satellite data compression scheme.展开更多
This paper discusses the performance difference between full-spectrum and channel-selection assimilation scheme of hyperspectral infrared observation, e.g. CrIS</span><span style="font-family:""...This paper discusses the performance difference between full-spectrum and channel-selection assimilation scheme of hyperspectral infrared observation, e.g. CrIS</span><span style="font-family:""> </span><span style="font-family:Verdana;">and IASI, on improving the accuracy of initial condition</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">in numerical weather prediction. To accomplish this, we develop a 3D-Variational data assimilation system whose observation operator is a principal-component based fast radiative transfer model, which equips the direct assimilation of full-channel radiance from hyperspectral infrared sounders with high computational efficiency. This project’s primary goal is to demonstrate that assimilation of infrared observation in a full-channel mode could improve the accuracy of initial condition compared to selected-channel assimilation. Resu</span><span style="font-family:Verdana;">lts show that full-channel assimilation performs better than se</span><span style="font-family:Verdana;">lected-channel assimilation in modifying low and middle troposphere (1000 - 700 hPa, 700 - 400 hPa) temperature and water vapor field, while marginal improvements from temperature and water vapor field could be found over upper troposphere (400 - 100 hPa). This research also proves the feasibility of an alternative path to data assimilation for the full usage of hyperspectral infrared sounding observation in numerical weather prediction.展开更多
Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferome...Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.展开更多
In view of the shortage of using traditional methods to monitor chlorophyll content, hyperspectral technology was used to estimate the chlorophyll content of apple leaves rapidly, accurately and non-destructively. Bas...In view of the shortage of using traditional methods to monitor chlorophyll content, hyperspectral technology was used to estimate the chlorophyll content of apple leaves rapidly, accurately and non-destructively. Based on the data of hyperspectral reflectivity and SPAD value of normal apple leaves and the leaves under the stress of red spiders collected from the Wanjishan base in Tai an, the correlations of SPAD value with the original spectral reflectivity of apple leaves and its first derivative and between SPAD value and high spectral value were analyzed to select sensitive bands, and the estimation models of chlorophyll content in apple leaves based on hyperspectral reflectivity were established. The sensitive bands of chlorophyll content in normal apple leaves were 513-539, 564-585, 694, 699 and 720 nm , and the best estimation model of chlorophyll content was SPAD =152.450-1 884.851 R 377 . The sensitive bands of chlorophyll content in the leaves under the stress of red spiders were 961, 972 and 720 nm, and the best estimation model of chlorophyll content was SPAD =49.371-46 428.473 R 972.展开更多
Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the Nile Delta and on the flood plain of the Nile River. More than 90% of this population relies on the Nile River in their ...Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the Nile Delta and on the flood plain of the Nile River. More than 90% of this population relies on the Nile River in their water demand for domestic use. Currently, Egypt is facing a problem with the trans-boundary water budget coming from the Nile basin. This urges for managing the water quantity and quality to secure the water needs. This paper discusses the potential use of airborne hyperspectral data for water quality management in the form of detecting the oil contamination in the Nile River in integration with in-situ measurements including ASD spectroradiometer and eco-sounder multi-probe devices. The eco-sounder multi-probe device measured most of the water quality parameters and detected the existence of oil contamination at 1200 bb downstream of the study area. The airborne hyperspectral images were analyzed and calibrated with the spectral library determined from the in-situ spectroradiometer to map the patches of the oil contamination. The details of the findings and learning lessons are fully discussed in the paper.展开更多
High spectral resolution(or hyperspectral)infrared(IR)sounders onboard low earth orbiting satellites provide high vertical resolution atmospheric information for numerical weather prediction(NWP)models.In contrast,ima...High spectral resolution(or hyperspectral)infrared(IR)sounders onboard low earth orbiting satellites provide high vertical resolution atmospheric information for numerical weather prediction(NWP)models.In contrast,imagers on geostationary(GEO)satellites provide high temporal and spatial resolution which are important for monitoring the moisture associated with severe weather systems,such as rapidly developing local severe storms(LSS).A hyperspectral IR sounder onboard a geostationary satellite would provide four-dimensional atmospheric temperature,moisture,and wind profiles that have both high vertical resolution and high temporal/spatial resolutions.In this work,the added-value from a GEO-hyperspectral IR sounder is studied and discussed using a hybrid Observing System Simulation Experiment(OSSE)method.A hybrid OSSE is distinctively different from the traditional OSSE in that,(a)only future sensors are simulated from the nature run and(b)the forecasts can be evaluated using real observations.This avoids simulating the complicated observation characteristics of the current systems(but not the new proposed system)and allows the impact to be assessed against real observations.The Cross-track Infrared Sounder(CrIS)full spectral resolution(FSR)is assumed to be onboard a GEO for the impact studies,and the GEO CrIS radiances are simulated from the ECMWF Reanalysis v5(ERA5)with the hyperspectral IR all-sky radiative transfer model(HIRTM).The simulated GEO CrIS radiances are validated and the hybrid OSSE system is verified before the impact assessment.Two LSS cases from 2018 and 2019 are selected to evaluate the value-added impacts from the GEO CrIS-FSR data.The impact studies show improved atmospheric temperature,moisture,and precipitation forecasts,along with some improvements in the wind forecasts.An added-value,consisting of an overall 5%Root Mean Square Error(RMSE)reduction,was found when a GEO CrIS-FSR is used in replacement of LEO ones indicat-ing the potential for applications of data from a GEO hyperspectral IR sounder to improve local severe storm forecasts.展开更多
Near-InfraRed and Visible (Vis-NIR) spectroscopy is a promising tool allowing to quantify soil properties. It shows that information encoded in hyperspectral data can be useful after signal processing and model calibr...Near-InfraRed and Visible (Vis-NIR) spectroscopy is a promising tool allowing to quantify soil properties. It shows that information encoded in hyperspectral data can be useful after signal processing and model calibration steps, in order to estimate various soil properties throughout appropriate statistical models. However, one of the problems encountered in the case of hyperspectral data is related to information redundancy between different spectral bands. This redundancy is at the origin of multi-collinearity in the explanatory variables leading to unstable regression coefficients (and, difficult to interpret). Moreover, in hyperspectral spectrum, the information concerning the chemical specificity is spread over several wavelengths. Therefore, it is not wise to remove this redundancy because this removal affects both relevant and irrelevant hyperspectral information. In this study, the faced challenge is to optimize the estimation of some soil properties by exploiting all the spectral richness of the hyperspectral data by providing complementary rather than redundant information. To this end, a new reliable approach based on hyperspectral data analysis and partial least squares regression is proposed.展开更多
In order to obtain Pb content in soil quickly and efficiently,a multivariate linear regression(MLR) and a principal component regression(PCR) Pb content estimation model were established on the basis of hyperspectral ...In order to obtain Pb content in soil quickly and efficiently,a multivariate linear regression(MLR) and a principal component regression(PCR) Pb content estimation model were established on the basis of hyperspectral techniques,and their applicability in different soil types was evaluated.Results indicated that Pb exhibited strong spatial heterogeneity in the study area,and more than 82% of the samples exceeded the background value.In addition,the pollution range was large.Pb was sensitive in the nearinfrared band,and the correlation of absorbance(AB) was most significant of all the transformed forms.Both models achieved optimal stability and reliability when AB was used as an independent variable.Compared with the PCR model,the stability,fitting accuracy,and predictive power of the MLR model were superior with a coefficient of determination,root mean square error,and mean relative error of 0.724%,24.92%,and 28.22%,respectively.Both models could be applied to different soil types;however,MLR had better applicability compared with PCR.The PCR model that distinguished different soil types had better reliability than one that did not.Thus,the model established via hyperspectral techniques can achieve largearea,rapid,and efficient soil Pb content monitoring,which can provide technical support for the treatment of heavy metal pollution in soil.展开更多
Hyperspectral images can easily discriminate different materials due to their fine spectral resolution.However,obtaining a hyperspectral image(HSI)with a high spatial resolution is still a challenge as we are limited ...Hyperspectral images can easily discriminate different materials due to their fine spectral resolution.However,obtaining a hyperspectral image(HSI)with a high spatial resolution is still a challenge as we are limited by the high computing requirements.The spatial resolution of HSI can be enhanced by utilizing Deep Learning(DL)based Super-resolution(SR).A 3D-CNNHSR model is developed in the present investigation for 3D spatial super-resolution for HSI,without losing the spectral content.The 3DCNNHSR model was tested for the Hyperion HSI.The pre-processing of the HSI was done before applying the SR model so that the full advantage of hyperspectral data can be utilized with minimizing the errors.The key innovation of the present investigation is that it used 3D convolution as it simultaneously applies convolution in both the spatial and spectral dimensions and captures spatial-spectral features.By clustering contiguous spectral content together,a cube is formed and by convolving the cube with the 3D kernel a 3D convolution is realized.The 3D-CNNHSR model was compared with a 2D-CNN model,additionally,the assessment was based on higherresolution data from the Sentinel-2 satellite.Based on the evaluation metrics it was observed that the 3D-CNNHSR model yields better results for the SR of HSI with efficient computational speed,which is significantly less than previous studies.展开更多
Hyperspectral unmixing aims to acquire pure spectra of distinct substances(endmembers)and fractional abundances from highly mixed pixels.In this paper,a deep unmixing network framework is designed to deal with the noi...Hyperspectral unmixing aims to acquire pure spectra of distinct substances(endmembers)and fractional abundances from highly mixed pixels.In this paper,a deep unmixing network framework is designed to deal with the noise disturbance.It contains two parts:a three⁃dimensional convolutional autoencoder(denoising 3D CAE)which recovers data from noised input,and a restrictive non⁃negative sparse autoencoder(NNSAE)which incorporates a hypergraph regularizer as well as a l2,1⁃norm sparsity constraint to improve the unmixing performance.The deep denoising 3D CAE network was constructed for noisy data retrieval,and had strong capacity of extracting the principle and robust local features in spatial and spectral domains efficiently by training with corrupted data.Furthermore,a part⁃based nonnegative sparse autoencoder with l2,1⁃norm penalty was concatenated,and a hypergraph regularizer was designed elaborately to represent similarity of neighboring pixels in spatial dimensions.Comparative experiments were conducted on synthetic and real⁃world data,which both demonstrate the effectiveness and robustness of the proposed network.展开更多
文摘Target detection is always an important application in hyperspectral image processing field. In this paper, a spectral-spatial target detection algorithm for hyperspectral data is proposed.The spatial feature and spectral feature were unified based on the data filed theory and extracted by weighted manifold embedding. The novelties of the proposed method lie in two aspects. One is the way in which the spatial features and spectral features were fused as a new feature based on the data field theory, and the other is that local information was introduced to describe the decision boundary and explore the discriminative features for target detection. The extracted features based on data field modeling and manifold embedding techniques were considered for a target detection task.Three standard hyperspectral datasets were considered in the analysis. The effectiveness of the proposed target detection algorithm based on data field theory was proved by the higher detection rates with lower False Alarm Rates（FARs） with respect to those achieved by conventional hyperspectral target detectors.
基金Supported by the National Natural Science Foundation of China(No.40971191)the Scientific Research Starting Foundation of Ministry of Education of China for Returned Overseas Chinese Scholarsthe Special Foundation of Ministry of Finance of China for Nonprofit Research of Forestry Industry(No.200904001)
文摘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 ex-plain 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-1;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 esti-mating 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.
基金supported by the Theory and Method of Excavation-Support-Anchor Parallel Control for Intelligent Excavation Complex System(2021101030125)Green,intelligent,and safe mining of coal resources(52121003)the Mining Robotics Engineering Discipline Innovation and Intelligence Base(B21014).
文摘Aiming at the problem of coal gangue identifcation in the current fully mechanized mining face and coal washing,this article proposed a convolution neural network(CNN)coal and rock identifcation method based on hyperspectral data.First,coal and rock spectrum data were collected by a near-infrared spectrometer,and then four methods were used to flter 120 sets of collected data:frst-order diferential(FD),second-order diferential(SD),standard normal variable transformation(SNV),and multi-style smoothing.The coal and rock refectance spectrum data were pre-processed to enhance the intensity of spectral refectance and absorption characteristics,as well as efectively remove the spectral curve noise generated by instrument performance and environmental factors.A CNN model was constructed,and its advantages and disadvantages were judged based on the accuracy of the three parameter combinations(i.e.,the learning rate,the number of feature extraction layers,and the dropout rate)to generate the best CNN classifer for the hyperspectral data for rock recognition.The experiments show that the recognition accuracy of the one-dimensional CNN model proposed in this paper reaches 94.6%.Verifcation of the advantages and efectiveness of the method were proposed in this article.
基金supported by the National Natural Science Foundation of China (Grant No.42101403)the National Key Researchand Development Program of China (Grant No.2017YFD0600404)。
文摘Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large,forested areas influenced by both the effects of bidirectional reflectance distribution function(BRDF)and cloud shadow contamination.In this study,hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry's LiDAR,CCD,and hyperspectral systems(CAF-LiCHy).After BRDF correction and cloud shadow detection processing,a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands,spectral vegetation indices,and texture information.Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data.Cloud-shaded pixels also have good spectral separability for species classification.The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions.According to the classification accuracies through field survey data at multiple spatial scales,it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.
文摘Hyperspectral data fusion technique is the key to hyperspectral data processing in recent years. Many fusion methods have been proposed, but little research has been done to evaluate the performances of different data fusion methods. In order to meet the urgent need, quantitative correlation analysis(QCA) is proposed to analyse and compare the performances of different fusion methods directly from data before and after fusion. Experiment results show that the new method is effective and the results of comparison are in agreement with the results of application.
文摘Hyperspectral images have wide applications in the fields of geology,mineral exploration,agriculture,forestry and environmental studies etc.due to their narrow band width with numerous channels.However,these images commonly suffer from atmospheric effects,thereby limiting their use.In such a situation,atmospheric correction becomes a necessary pre-requisite for any further processing and accurate interpretation of spectra of different surface materials/objects.In the present study,two very advance atmospheric approaches i.e.QUAC and FLAASH have been applied on the hyperspectral remote sensing imagery.The spectra of vegetation,man-made structure and different minerals from the Gadag area of Karnataka,were extracted from the raw image and also from the QUAC and FLAASH corrected images.These spectra were compared among themselves and also with the existing USGS and JHU spectral library.FLAASH is rigorous atmospheric algorithm and requires various parameters to perform but it has capability to compensate the effects of atmospheric absorption.These absorption curves in any spectra play an important role in identification of the compositions.Therefore,the presence of unwanted absorption features can lead to wrong interpretation and identification of mineral composition.FLAASH also has an advantage of spectral polishing which provides smooth spectral curves which helps in accurate identification of composition of minerals.Therefore,this study recommends that FLAASH is better than QUAC for atmospheric correction and correct interpretation and identification of composition of any object or minerals.
基金funded by China Geological Survey (grant no.1212011120899)the Department of Geology & Mining, China National Nuclear Corporation (grant no.201498)
文摘Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.
基金Supported by the National Natural Science Foundation of China （ No. 60872083 ） and the National High Technology Research and Development Program of China （No. 2007AA12Z149）.
文摘This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is proposed, namely minimum distance constrained nonnegative matrix factoriza- tion （MDC-NMF）. In this paper, firstly, a new regularization term, called endmember distance （ED） is considered, which is defined as the sum of the squared Euclidean distances from each end- member to their geometric center. Compared with the simplex volume, ED has better optimization properties and is conceptually intuitive. Secondly, a projected gradient （PG） scheme is adopted, and by the virtue of ED, in this scheme the optimal step size along the feasible descent direction can be calculated easily at each iteration. Thirdly, a finite step （ no more than the number of endmem- bers） terminated algorithm is used to project a point on the canonical simplex, by which the abun- dance nonnegative constraint and abundance sum-to-one constraint can be accurately satisfied in a light amount of computation. The experimental results, based on a set of synthetic data and real da- ta, demonstrate that, in the same running time, MDC-NMF outperforms several other similar meth- ods proposed recently.
基金Under the auspices of National Natural Science Foundation of China(No.41230751,41101547)Scientific Research Foundation of Graduate School of Nanjing University(No.2012CL14)
文摘Hyperspectral data are an important source for monitoring soil salt content on a large scale. However, in previous studies, barriers such as interference due to the presence of vegetation restricted the precision of mapping soil salt content. This study tested a new method for predicting soil salt content with improved precision by using Chinese hyperspectral data, Huan Jing-Hyper Spectral Imager(HJ-HSI), in the coastal area of Rudong County, Eastern China. The vegetation-covered area and coastal bare flat area were distinguished by using the normalized differential vegetation index at the band length of 705 nm(NDVI705). The soil salt content of each area was predicted by various algorithms. A Normal Soil Salt Content Response Index(NSSRI) was constructed from continuum-removed reflectance(CR-reflectance) at wavelengths of 908.95 nm and 687.41 nm to predict the soil salt content in the coastal bare flat area(NDVI705 < 0.2). The soil adjusted salinity index(SAVI) was applied to predict the soil salt content in the vegetation-covered area(NDVI705 ≥ 0.2). The results demonstrate that 1) the new method significantly improves the accuracy of soil salt content mapping(R2 = 0.6396, RMSE = 0.3591), and 2) HJ-HSI data can be used to map soil salt content precisely and are suitable for monitoring soil salt content on a large scale.
基金supported by the Chinese 863 Plan Program under Grant 2012AA121504
文摘Based on the raw data of spaceborne dispersive and interferometry imaging spectrometer,a set of quality evaluation metrics for compressed hyperspectral data is initially established in this paper.These quality evaluation metrics,which consist of four aspects including compression statistical distortion,sensor performance evaluation,data application performance and image quality,are suited to the comprehensive and systematical analysis of the impact of lossy compression in spaceborne hyperspectral remote sensing data quality.Furthermore,the evaluation results would be helpful to the selection and optimization of satellite data compression scheme.
文摘This paper discusses the performance difference between full-spectrum and channel-selection assimilation scheme of hyperspectral infrared observation, e.g. CrIS</span><span style="font-family:""> </span><span style="font-family:Verdana;">and IASI, on improving the accuracy of initial condition</span><span style="font-family:""> </span><span style="font-family:""><span style="font-family:Verdana;">in numerical weather prediction. To accomplish this, we develop a 3D-Variational data assimilation system whose observation operator is a principal-component based fast radiative transfer model, which equips the direct assimilation of full-channel radiance from hyperspectral infrared sounders with high computational efficiency. This project’s primary goal is to demonstrate that assimilation of infrared observation in a full-channel mode could improve the accuracy of initial condition compared to selected-channel assimilation. Resu</span><span style="font-family:Verdana;">lts show that full-channel assimilation performs better than se</span><span style="font-family:Verdana;">lected-channel assimilation in modifying low and middle troposphere (1000 - 700 hPa, 700 - 400 hPa) temperature and water vapor field, while marginal improvements from temperature and water vapor field could be found over upper troposphere (400 - 100 hPa). This research also proves the feasibility of an alternative path to data assimilation for the full usage of hyperspectral infrared sounding observation in numerical weather prediction.
文摘Among hyperspectral imaging technologies, interferometric spectral imaging is widely used in remote sening due to advantages of large luminous flux and high resolution. However, with complicated mechanism, interferometric imaging faces the impact of multi-stage degradation. Most exsiting interferometric spectrum reconstruction methods are based on tradition model-based framework with multiple steps, showing poor efficiency and restricted performance. Thus, we propose an interferometric spectrum reconstruction method based on degradation synthesis and deep learning.Firstly, based on imaging mechanism, we proposed an mathematical model of interferometric imaging to analyse the degradation components as noises and trends during imaging. The model consists of three stages, namely instrument degradation, sensing degradation, and signal-independent degradation process. Then, we designed calibration-based method to estimate parameters in the model, of which the results are used for synthesizing realistic dataset for learning-based algorithms.In addition, we proposed a dual-stage interferogram spectrum reconstruction framework, which supports pre-training and integration of denoising DNNs. Experiments exhibits the reliability of our degradation model and synthesized data, and the effectiveness of the proposed reconstruction method.
基金Supported by Innovation Engineering Project of Shandong Academy of Agricultural Sciences(CXGC2017B04)Major Research and Development Plan Program of Shandong Province,China(2016CYJS03A01-1)
文摘In view of the shortage of using traditional methods to monitor chlorophyll content, hyperspectral technology was used to estimate the chlorophyll content of apple leaves rapidly, accurately and non-destructively. Based on the data of hyperspectral reflectivity and SPAD value of normal apple leaves and the leaves under the stress of red spiders collected from the Wanjishan base in Tai an, the correlations of SPAD value with the original spectral reflectivity of apple leaves and its first derivative and between SPAD value and high spectral value were analyzed to select sensitive bands, and the estimation models of chlorophyll content in apple leaves based on hyperspectral reflectivity were established. The sensitive bands of chlorophyll content in normal apple leaves were 513-539, 564-585, 694, 699 and 720 nm , and the best estimation model of chlorophyll content was SPAD =152.450-1 884.851 R 377 . The sensitive bands of chlorophyll content in the leaves under the stress of red spiders were 961, 972 and 720 nm, and the best estimation model of chlorophyll content was SPAD =49.371-46 428.473 R 972.
文摘Egypt is a highly populated country of about 85 million inhabitants that are concentrated on the Nile Delta and on the flood plain of the Nile River. More than 90% of this population relies on the Nile River in their water demand for domestic use. Currently, Egypt is facing a problem with the trans-boundary water budget coming from the Nile basin. This urges for managing the water quantity and quality to secure the water needs. This paper discusses the potential use of airborne hyperspectral data for water quality management in the form of detecting the oil contamination in the Nile River in integration with in-situ measurements including ASD spectroradiometer and eco-sounder multi-probe devices. The eco-sounder multi-probe device measured most of the water quality parameters and detected the existence of oil contamination at 1200 bb downstream of the study area. The airborne hyperspectral images were analyzed and calibrated with the spectral library determined from the in-situ spectroradiometer to map the patches of the oil contamination. The details of the findings and learning lessons are fully discussed in the paper.
基金This work is supported by the NOAA GeoXO program(NA15NES4320001).
文摘High spectral resolution(or hyperspectral)infrared(IR)sounders onboard low earth orbiting satellites provide high vertical resolution atmospheric information for numerical weather prediction(NWP)models.In contrast,imagers on geostationary(GEO)satellites provide high temporal and spatial resolution which are important for monitoring the moisture associated with severe weather systems,such as rapidly developing local severe storms(LSS).A hyperspectral IR sounder onboard a geostationary satellite would provide four-dimensional atmospheric temperature,moisture,and wind profiles that have both high vertical resolution and high temporal/spatial resolutions.In this work,the added-value from a GEO-hyperspectral IR sounder is studied and discussed using a hybrid Observing System Simulation Experiment(OSSE)method.A hybrid OSSE is distinctively different from the traditional OSSE in that,(a)only future sensors are simulated from the nature run and(b)the forecasts can be evaluated using real observations.This avoids simulating the complicated observation characteristics of the current systems(but not the new proposed system)and allows the impact to be assessed against real observations.The Cross-track Infrared Sounder(CrIS)full spectral resolution(FSR)is assumed to be onboard a GEO for the impact studies,and the GEO CrIS radiances are simulated from the ECMWF Reanalysis v5(ERA5)with the hyperspectral IR all-sky radiative transfer model(HIRTM).The simulated GEO CrIS radiances are validated and the hybrid OSSE system is verified before the impact assessment.Two LSS cases from 2018 and 2019 are selected to evaluate the value-added impacts from the GEO CrIS-FSR data.The impact studies show improved atmospheric temperature,moisture,and precipitation forecasts,along with some improvements in the wind forecasts.An added-value,consisting of an overall 5%Root Mean Square Error(RMSE)reduction,was found when a GEO CrIS-FSR is used in replacement of LEO ones indicat-ing the potential for applications of data from a GEO hyperspectral IR sounder to improve local severe storm forecasts.
文摘Near-InfraRed and Visible (Vis-NIR) spectroscopy is a promising tool allowing to quantify soil properties. It shows that information encoded in hyperspectral data can be useful after signal processing and model calibration steps, in order to estimate various soil properties throughout appropriate statistical models. However, one of the problems encountered in the case of hyperspectral data is related to information redundancy between different spectral bands. This redundancy is at the origin of multi-collinearity in the explanatory variables leading to unstable regression coefficients (and, difficult to interpret). Moreover, in hyperspectral spectrum, the information concerning the chemical specificity is spread over several wavelengths. Therefore, it is not wise to remove this redundancy because this removal affects both relevant and irrelevant hyperspectral information. In this study, the faced challenge is to optimize the estimation of some soil properties by exploiting all the spectral richness of the hyperspectral data by providing complementary rather than redundant information. To this end, a new reliable approach based on hyperspectral data analysis and partial least squares regression is proposed.
基金supported jointly by National Key Research Program of China (Nos. 2016YFC0502300 and 2016YFC0502102)Chinese Academy of Science, and Technology Services Network Program (No. KFJ-STS-ZDTP-036)+4 种基金International Cooperation Agency International Partnership Program (Nos. 132852KYSB20170029, 2014-3)Guizhou High-level Innovative Talent Training Program “Ten” Level Talents Program (No. 2016-5648)United Fund of Karst Science Research Center (No. U1612441)International Cooperation Research Projects of the National Natural Science Fund Committee (Nos. 41571130074 and 41571130042)Science and Technology Plan of Guizhou Province of China (No. 2017–2966)
文摘In order to obtain Pb content in soil quickly and efficiently,a multivariate linear regression(MLR) and a principal component regression(PCR) Pb content estimation model were established on the basis of hyperspectral techniques,and their applicability in different soil types was evaluated.Results indicated that Pb exhibited strong spatial heterogeneity in the study area,and more than 82% of the samples exceeded the background value.In addition,the pollution range was large.Pb was sensitive in the nearinfrared band,and the correlation of absorbance(AB) was most significant of all the transformed forms.Both models achieved optimal stability and reliability when AB was used as an independent variable.Compared with the PCR model,the stability,fitting accuracy,and predictive power of the MLR model were superior with a coefficient of determination,root mean square error,and mean relative error of 0.724%,24.92%,and 28.22%,respectively.Both models could be applied to different soil types;however,MLR had better applicability compared with PCR.The PCR model that distinguished different soil types had better reliability than one that did not.Thus,the model established via hyperspectral techniques can achieve largearea,rapid,and efficient soil Pb content monitoring,which can provide technical support for the treatment of heavy metal pollution in soil.
基金Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under Grant Number RGP2/80/44.
文摘Hyperspectral images can easily discriminate different materials due to their fine spectral resolution.However,obtaining a hyperspectral image(HSI)with a high spatial resolution is still a challenge as we are limited by the high computing requirements.The spatial resolution of HSI can be enhanced by utilizing Deep Learning(DL)based Super-resolution(SR).A 3D-CNNHSR model is developed in the present investigation for 3D spatial super-resolution for HSI,without losing the spectral content.The 3DCNNHSR model was tested for the Hyperion HSI.The pre-processing of the HSI was done before applying the SR model so that the full advantage of hyperspectral data can be utilized with minimizing the errors.The key innovation of the present investigation is that it used 3D convolution as it simultaneously applies convolution in both the spatial and spectral dimensions and captures spatial-spectral features.By clustering contiguous spectral content together,a cube is formed and by convolving the cube with the 3D kernel a 3D convolution is realized.The 3D-CNNHSR model was compared with a 2D-CNN model,additionally,the assessment was based on higherresolution data from the Sentinel-2 satellite.Based on the evaluation metrics it was observed that the 3D-CNNHSR model yields better results for the SR of HSI with efficient computational speed,which is significantly less than previous studies.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61876054)the National Key Research and Development Program of China(Grant No.2019YFC0117400).
文摘Hyperspectral unmixing aims to acquire pure spectra of distinct substances(endmembers)and fractional abundances from highly mixed pixels.In this paper,a deep unmixing network framework is designed to deal with the noise disturbance.It contains two parts:a three⁃dimensional convolutional autoencoder(denoising 3D CAE)which recovers data from noised input,and a restrictive non⁃negative sparse autoencoder(NNSAE)which incorporates a hypergraph regularizer as well as a l2,1⁃norm sparsity constraint to improve the unmixing performance.The deep denoising 3D CAE network was constructed for noisy data retrieval,and had strong capacity of extracting the principle and robust local features in spatial and spectral domains efficiently by training with corrupted data.Furthermore,a part⁃based nonnegative sparse autoencoder with l2,1⁃norm penalty was concatenated,and a hypergraph regularizer was designed elaborately to represent similarity of neighboring pixels in spatial dimensions.Comparative experiments were conducted on synthetic and real⁃world data,which both demonstrate the effectiveness and robustness of the proposed network.
