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Semi-Supervised Classification based on Gaussian Mixture Model for remote imagery

Science china(Technological Sciences) 2010年第S1期53卷 85-90页

作者： xiong biao1,zhang xiaojun1 & jiang wanshou1,2.1 {2. key laboratory of information engineering in surveying,mapping and remote sensing,wuhan university,wuhan 43.079,china 2.{2. key laboratory of remote sensing Science,Beijing,china 1. state key {2. of information engineering in surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 China2. State Key Laboratory of Remote Sensing Science Beijing China

Semi-Supervised Classification (SSC),which makes use of both labeled and unlabeled data to determine classification borders in feature space,has great advantages in extracting classification information from mass *** this paper,a novel SSC method based on Gaussian Mixture Model (GMM) is proposed,in which each class’s feature space is described by one *** show the proposed method can achieve high classification accuracy with small amount of labeled ***,for the same accuracy,supervised classification methods such as Support Vector Machine,Object Oriented Classification,*** be provided with much more labeled data.

关键词： remote sensing image classification Semi-Supervised Classification Gaussian Mixture Model EM algorithms

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Proximal and remote sensing in plant phenomics: 2. years of progress, challenges, and perspectives

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Plant Communications 2022年第6期3卷 135-173页

作者： Haiyu Tao Shan Xu Yongchao Tian Zhaofeng Li Yan Ge Jiaoping zhang Yu Wang Guodong Zhou xiong Deng Ze zhang Yanfeng Ding Dong jiang Qinghua Guo Shichao Jin Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesNational Engineering and Technology Center for Information AgricultureCollaborative Innovation Centre for Modern Crop Production co-sponsored by Province and MinistryNanjing Agricultural UniversityAddress:No.1 WeigangXuanwu DistrictNanjing 210095China The key laboratory of Oasis Eco-agriculture Xinjiang Production and Construction CorpsAgriculture CollegeShihezi UniversityShihezi 832003China Hainan Yazhou Bay Seed laboratory Sanya 572025China state key {2. of Crop Genetics and Germplasm Enhancement National Center for Soybean ImprovementKey Laboratory for Biology and Genetic Improvement of Soybean(GeneralMinistry of Agriculture)Nanjing Agricultural UniversityNanjing 210095China Sanya Research Institute of Nanjing Agriculture university Sanya 572024China key laboratory of Plant Molecular Physiology Institute of BotanyChinese Academy of SciencesBeijing 100093China Institute of Ecology College of Urban and Environmental SciencePeking UniversityBeijing 100871China jiangsu Provincial key {2. of Geographic information Science and Technology International Institute for Earth System SciencesNanjing UniversityNanjingJiangsu 210023China

Plant phenomics(PP)has been recognized as a bottleneck in studying the interactions of genomics and environment on plants,limiting the progress of smart breeding and precise ***-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of *** and remote sensing(PRS)techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and *** progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2.72.*** progress covers most aspects of PRS application in PP,including patterns of global spatial distribution and temporal dynamics,specific PRS technologies,phenotypic research fields,working environments,species,and ***,we demonstrate how to link PRS to multi-omics studies,including how to achieve multi-dimensional PRS data acquisition and processing,how to systematically integrate all kinds of phenotypic {2. and derive phenotypic knowledge with biological significance,and how to link PP to multi-omics association ***,we identify three future perspectives for PRS-based PP:(1)strengthening the spatial and temporal consistency of PRS data,(2.exploring novel phenotypic traits,and(3)facilitating multi-omics communication.

关键词： plant phenomics remote sensing phenotyping phenotypic traits multi-omics breeding precision cultivation

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