Study of Spectral Response Characteristics of Oilseed Rape (Brassica napus) to Particulate Matters Based on Hyper-Spectral Technique

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.

Spatial-Aware Supervised Learning for Hyper-Spectral Image Classification Comprehensive Assessment

A comprehensive assessment of the spatial-aware supervised learning algorithms for hyper-spectral image(HSI)classification was presented.For this purpose,standard support vector machines(SVMs),multinomial 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 probabilistic 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 misclassification error although the cost of computational time will be increased.

未来光电空对地瞄准展望

为了应对未来空对地瞄准的需求,不仅要改善当前空对地瞄准的能力,而且要探索新的作战概念。本文结合最新的光电技术,提出了提升目前机载光电瞄准系统性能的途径,同时针对当前空对地作战发生的变化,提出了基于ISR的空对地瞄准概念,并对可能用到的新技术进行了描述。

Hydrocarbon Micro-Seepage Detection by Altered Minerals Mapping from Airborne Hyper-Spectral Data in Xifeng Oilfield,China

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.

Distance-based separability criterion of ROI in classification of farmland hyper-spectral images

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.

Radiometric calibration of hyper-spectral imaging spectrometer based on optimizing multi-spectral band selection

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.