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Study on shallow groundwater information extraction technology based on multi-spectral data and spatial data

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Science China(Technological Sciences) 2009年第5期52卷 1420-1428页

作者： YU DeHao DENG ZhengDong LONG Fan GUAN HongJun WANG DaQing GOU YiZheng Engineering Institute of Engineering Corps PLA University of Science and TechnologyNanjing 210007China Engineering Research Institute of Shenyang Military Area Command Shenyang 110162China

Aimed at solving the difficulties,such as low efficiency and limited exploration range encountered in finding groundwater with the traditional methods,a new method was presented by using remote sensing technology in this *** on multi-spectral data(ETM data) and spatial data(SRTM data),a forecasting model was built to produce a probability rating map for finding shallow groundwater in the arid and semi-arid *** to investigations,a conclusion is drawn that the results of the model are satisfied,which have been testified by the later geophysical exploration and ***,the model can serve as a guide for finding groundwater in the arid and semi-arid regions.

关键词： remote sensing shallow groundwater forecasting model multi-spectral data spatial data

New Abnormal Cervical Cell Detection Method of multi-spectral Pap Smears

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Wuhan University Journal of Natural Sciences 2007年第3期12卷 476-480页

作者： CAO Feng CHEN Shuzhen ZENG Libo School of Electronic Information Wuhan University Wuhan 430072 Hubei China

Considering the problem of traditional cervical cancer detection method that brings high false negative rate （FNR） and high false positive rate （FPR）, a new abnormal cervical cells detection method of multi-spectral Pap smear is proposed in this thesis, on the basis of multi-spectral microscopic imaging technology and computer automotive recognition technology. At first, image in a specific wave band is segmented according to the relationship between intensity and spectrum of each pixel. Then, multi-spectral features of each pixel are extracted making use of improved cosine correlation analysis （CCA） algorithm. Combined with the characteristic of each cell＇s area, final definition is made. Experiments have proved the new approach could identify abnormal cells efficiently as well as lower FNR and FPR.

关键词： multi-spectral cervical pap smears improved CCA

multi-spectral and Fluorescence Imaging in Prevention of Overdose of Herbicides: The Case of Maize

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spectral Analysis Review 2017年第2期5卷 11-24页

作者： Anicet K. Kouakou Adama P. Soro Alvarez K. Taky Jérémie T. Zoueu Laboratoire de Cristallographie et Physique Moléculaire Université Félix Houphou&euml t Boigny Abidjan C&ocirc te d’Ivoire Laboratoire d’Instrumentation Image et Spectroscopie INP-HB DFR-GEE Yamoussoukro C&ocirc te d’Ivoire

Evaluation of the impact of herbicides on maize was done through multi- spectral and multi-modal imaging and multi-spectral fluorescence imaging combined with statistical methods. Spectra containing 13 wavelengths ranging from 375 nm to 940 nm were derived from multi-spectral images in transmission, reflection and scattering mode and fluorescence images obtained using high-pass filters (F450 nm, F500 nm, F550 nm, F600 nm, F650 nm) on control maize samples and maize samples treated with Herbextra herbicide were used. The appearance of the spectra allowed us to characterize the effect of the herbicide on the maize pigment concentration. The fluorescence images allowed us to track the fate of absorbed energy and through PLS-DA and SVM-DA to discriminate the two leaf categories with very low error rates for the test, i.e. 4.9% and 2% respectively. The results of this technique can be used in the context of precision agriculture.

关键词： Maize Herbextra multi-spectral Imaging multimodal Imaging Fluorescence PLS-DA SVM-DA

Chlorophyll Content Retrieval of Rice Canopy with multi-spectral Inversion Based on LS-SVR Algorithm

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Journal of Northeast Agricultural University(English Edition) 2019年第1期26卷 53-63页

作者： Jin Si-yu Su Zhong-bin Xu Zhe-nan Jia Yin-jiang Yan Yu-guang Jiang Tao College of Electrical and Information Northeast Agricultural University

To monitor growth and predict the yield of rice over a large area, the chlorophyll contents in the rice canopy were estimated using the unmanned aerial vehicle(UAV) remote sensing technology. In this work, multi-spectral image information of the rice crop was obtained using a 6-channel multi-spectral camera mounted on a fixed wing UAV, which was flown 600 m above the ground, between 11: 00-14: 00 on a sunny day in summer. The measured chlorophyll values were collected as sample sets. The s-REP index was screened out to estimate chlorophyll contents through the analysis of six kinds of spectral indexes of chlorophyll estimated capacity. An inversion model of the chlorophyll contents was then built using the least square support vector regression(LS-SVR)algorithm, with calibration and prediction R-square values of 0.89 and 0.83, respectively. Finally, remote sensing mapping for a UAV image of the Fangzheng County Dexter Rice Planting Park was accomplished using the inversion model. The inversion and measured values were then compared using regression fitting. R-square and root-mean-square error of the fitting model were 0.79 and 2.39,respectively. The results demonstrated that accurate estimation of rice-canopy chlorophyll contents was feasible using the LS-SVR inversion model developed using the s-REP vegetation index.

关键词： remote sensing chlorophyll rice UAV multi-spectral inversion LS-SVR

Extraction of Soil Organic Matter Information by multi-spectral Remote Sensing Based on Diverse Landforms

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Agricultural Science & Technology 2016年第7期17卷 1744-1748页

作者：杨建锋马军成王令超樊鹏河南省科学院地理研究所河南郑州450052

Based on diverse landforms, the correlation between soil organic matter content and multi-spectral band of remote sensing image was analyzed in this pa- per. In addition, the inversion models were built for the soil organic matter content in different landforms. The results showed that the spectral reflectance was nega- tively related to soil organic matter content; linear regression analysis of remove was performed throughout the bands using SPSS. When the inversion models were built based on all the bands, better fitting effect was obtained. The precision of in- version models built based on different landforms was higher than those built re- gardless landforms. Compared with the actual value, the identification level of soil organic matter content was 91 65% under the allowable error was 7%. It indicated that the extraction of soil organic matter with inversion model that was built based on different landforrrs was feasible with higher precision.

关键词： Landform type multi-spectral Regression analysis Soil organic matter

Satellite-based Observational Study of the Tibetan Plateau Vortex:Features of Deep Convective Cloud Tops

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Advances in Atmospheric Sciences 2019年第2期36卷 189-205,248页

作者： Yi-Xuan SHOU Feng LU Hui LIU Peng CUI Shaowen SHOU Jian LIU Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites China Meteorological Administration(LRCVES/CMA) National Satellite Meteorological Center Key Laboratory of Meteorological Disasters Nanjing University of Information Sciences and Technology

In this study, an east-moving Tibetan Plateau vortex(TPV) is analyzed by using the ERA-5 reanalysis and multi-source satellite data, including FengYun-2 E, Aqua/MODIS and CALIPSO. The objective is to demonstrate:(i) the usefulness of multi-spectral satellite observations in understanding the evolution of a TPV and the associated rainfall, and(ii) the potential significance of cloud-top quantitative information in improving Southwest China weather forecasts. Results in this study show that the heavy rainfall is caused by the coupling of an east-moving TPV and some low-level weather systems [a Plateau shear line and a Southwest Vortex(SWV)], wherein the TPV is a key component. During the TPV's life cycle, the rainfall and vortex intensity maintain a significant positive correlation with the convective cloud-top fraction and height within a 2.5?radius away from its center. Moreover, its growth is found to be quite sensitive to the cloud phases and particle sizes. In the mature stage when the TPV is coupled with an SWV, an increase of small ice crystal particles and appearance of ring-and U/V-shaped cold cloud-top structures can be seen as the signature of a stronger convection and rainfall enhancement within the TPV. A tropopause folding caused by ageostrophic flows at the upper level may be a key factor in the formation of ring-shaped and U/V-shaped cloud-top structures. Based on these results, we believe that the supplementary quantitative information of an east-moving TPV cloud top collected by multi-spectral satellite observations could help to improve Southwest China short-range/nowcasting weather forecasts.

关键词： Tibetan Plateau vortex multi-spectral satellite observations short-range/nowcasting weather forecasts cold U/V-shaped cloud top tropopause folding

Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests

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Forest Ecosystems 2021年第4期8卷 810-830页

作者： Juan Guerra-Hernandez Ramon A.Diaz-Varela Juan Gabriel Avarez-Gonzalez Patricia Maria Rodriguez-Gonzalez edata Centro de iniciativas empresariaisFundacion CEL.O Palomar s/n27004 LugoSpain Forest Research Centre School of AgricultureUniversity of LisbonInstituto Superior de Agronomia(ISA)Tapada da Ajuda1349-017 LisbonPortugal Biodiversity and Applied Botany Unit(GI BIOAPLIC 1809) Department of BotanyEscuela Politecnica SuperiorR/Benigno LedoUniversity of Santiago de CompostelaCampus Universitario27002 LugoSpain Sustainable Forest Management Unit(UXFS) Department of Agroforestry EngineeringEscuela Politecnica SuperiorR/Benigno LedoUniversity of Santiago de CompostelaCampus UniversitarioE-27002 LugoSpain

Background:Black alder(Alnus glutinosa)forests are in severe decline across their area of distribution due to a disease caused by the soil-borne pathogenic Phytophthora alni species complex(class Oomycetes),“alder Phytopththora”.Mapping of the different types of damages caused by the disease is challenging in high density ecosystems in which spectral variability is high due to canopy *** obtained by unmanned aerial vehicles(UAVs)may be particularly useful for such tasks due to the high resolution,flexibility of acquisition and cost efficiency of this type of *** this study,*** decline was assessed by considering four categories of tree health status in the field:asymptomatic,dead and defoliation above and below a 50% threshold.A combination of multispectral Parrot Sequoia and UAV unmanned aerial vehicles-red green blue(RGB)data were analysed using classical random forest(RF)and a simple and robust three-step logistic modelling approaches to identify the most important forest health indicators while adhering to the principle of parsimony.A total of 34 remote sensing variables were considered,including a set of vegetation indices,texture features from the normalized difference vegetation index(NDVI)and a digital surface model(DSM),topographic and digital aerial photogrammetry-derived structural data from the DSM at crown ***:The four categories identified by the RF yielded an overall accuracy of 67%,while aggregation of the legend to three classes(asymptomatic,defoliated,dead)and to two classes(alive,dead)improved the overall accuracy to 72% and 91% *** the other hand,the confusion matrix,computed from the three logistic models by using the leave-out cross-validation method yielded overall accuracies of 75%,80% and 94% for four-,three-and two-level classifications,***:The study findings provide forest managers with an alternative robust classification method for the rapid,effective assessment of area

关键词： Alder RPAS multi-spectral Defoliation Texture variables 3D point cloud Tree health monitoring

USING COVARIANCE INTERSECTION FOR CHANGE DETECTION IN REMOTE SENSING IMAGES

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Journal of Electronics(China) 2011年第1期28卷 87-94页

作者： Yang Meng Zhang Gong Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

In this paper,an unsupervised change detection technique for remote sensing images acquired on the same geographical area but at different time instances is proposed by conducting Covariance Intersection(CI) to perform unsupervised fusion of the final fuzzy partition matrices from the Fuzzy C-Means(FCM) clustering for the feature space by applying compressed sampling to the given remote sensing *** proposed approach exploits a CI-based data fusion of the membership function matrices,which are obtained by taking the Fuzzy C-Means(FCM) clustering of the frequency-domain feature vectors and spatial-domain feature vectors,aimed at enhancing the unsupervised change detection *** sampling is performed to realize the image local feature sampling,which is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable *** experimental results demonstrate that the proposed algorithm has a good change detection results and also performs quite well on denoising purpose.

关键词： image Change detection Covariance Intersection (CI) Fusion SAR image multi-spectral

Production of multi-Features Driven Nationwide Vegetation Physiognomic Map and Comparison to MODIS Land Cover Type Product

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Advances in Remote Sensing 2017年第1期6卷 54-65页

作者： Ram C. Sharma Keitarou Hara Hidetake Hirayama Ippei Harada Daisuke Hasegawa Mizuki Tomita Jong Geol Park Ichio Asanuma Kevin M. Short Masatoshi Hara Yoshihiko Hirabuki Michiro Fujihara Ryutaro Tateishi Department of Informatics Tokyo University of Information Sciences Chiba Japan Center for Environmental Remote Sensing Chiba University Chiba Japan Graduate School of Informatics Tokyo University of Information Sciences Chiba Japan Foundation of River and Basin Integrated Communications Tokyo Japan Natural History Museum and Institute Chiba Japan Department of Regional Design Tohoku Gakuin University Sendai Japan Awaji Landscape Planning and Horticulture Academy University of Hyogo Hyogo Japan

Irrespective of several attempts to land use/cover mapping at local, regional, or global scales, mapping of vegetation physiognomic types is limited and challenging. The main objective of the research is to produce an accurate nationwide vegetation physiognomic map by using automated machine learning approach with the support of reference data. A time-series of the multi-spectral and multi-indices data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were exploited along with the land-surface slope data. Reliable reference data of the vegetation physiognomic types were prepared by refining the existing vegetation survey data available in the country. The Random Forests based mapping framework adopted in the research showed high performance (Overall accuracy = 0.82, Kappa coefficient = 0.79) using 148 optimum number of features out of 231 featured used. A nationwide vegetation physiognomic map of year 2013 was produced in the research. The resulted map was compared to the existing MODIS Land Cover Type (MCD12Q1) product of year 2013. A huge difference was found between two maps. Validation with the reference data showed that the MCD12Q1 product did not work satisfactorily in Japan. The outcome of the research highlights the possibility of improving the accuracy of the MCD12Q1 product with special focus on reference data.

关键词： Vegetation Physiognomy multi-spectral Topography MODIS Mapping Machine Learning Japan MCD12Q1