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Fungal diversity notes 1036-1150:taxonomic and phylogenetic contributions on genera and species of fungal taxa

Fungal Diversity 2019年第3期96卷 1-242页

作者： Kevin D.Hyde Danushka S.Tennakoon Rajesh Jeewon DJayarama Bhat Sajeewa S.N.Maharachchikumbura Walter Rossi Marco Leonardi Hyang Burm Lee Hye Yeon Mun Jos Houbraken Thuong T.T.Nguyen Sun Jeong Jeon Jens Christian Frisvad Dhanushka N.Wanasinghe Robert Lucking Andre Aptroot Marcela E.S.Caceres Samantha C.Karunarathna Sinang Hongsanan Rungtiwa Phookamsak Nimali Ide Silva Kasun M.Thambugala Ruvishika S.Jayawardena Indunil C.Senanayake Saranyaphat Boonmee Jie Chen Zong-Long Luo Chayanard Phukhamsakda Olinto L.Pereira Vanessa P.Abreu Andre Wilson Campos Rosado Buyck Bart Emile Randrianjohany Vale rie Hofstetter Tatiana B.Gibertoni Adriene Mayrada Silva Soares Helio Longoni Plautz Jr Helen Maria Pontes Sotao William Kalhy Silva Xavier Jadson Diogo Pereira Bezerra Thays Gabrielle Linsde Oliveira Cristina Mariade Souza-Motta Oliane Maria Correia Magalhaes Digvijayini Bundhun Dulanjalee Harishchandra Ishara S.Manawasinghe Wei Dong Sheng-Nan Zhang Dan-Feng Bao Milan C.Samarakoon Dhandevi Pem Anuruddha Karunarathna Chuan-Gen Lin Jing Yang Rekhani H.Perera Vinit Kumar Shi-Ke Huang Monika C.Dayarathne Anusha H.Ekanayaka Subashini C.Jayasiri Yuanpin Xiao Sirinapa Konta Tuula Niskanen Kare Liimatainen Yu-Cheng Dai Xiao-Hong Ji Xue-Mei Tian Armin Mesic Sanjay K.Singh Kunthida Phutthacharoen Lei Cai Touny Sorvongxay Vinodhini Thiyagaraja Chada Norphanphoun Napalai Chaiwan Yong-Zhong Lu Hong-Bo Jiang Jin-Feng Zhang Pranami D.Abeywickrama Janith V.S.Aluthmuhandiram Rashika S.Brahmanage Ming Zeng Thilini Chethana Deping Wei Martina Reblova Jacques Fournier Jana Nekvindova Renan do Nascimento Barbosa Jose Ewerton Felintodos Santos Neiva Tintide Oliveira Guo-Jie Li Damien Ertz Qiu-Ju Shang Alan J.L.Phillips Chang-Hsin Kuo Erio Camporesi Timur S.Bulgakov Saisamorn Lumyong E.B.Gareth Jones Putarak Chomnunti Eleni Gentekaki Frank Bungartz Xiang-Yu Zeng Sally Fryar Zdenko Tkalcec Junmin Liang Guangshuo Li Ting-Chi Wen Paras Nath Singh Yusufjon Gafforov Itthayakorn Promputtha Erandi Yasanthika Ishani D.Goonasekara Rui-Lin Zhao Qi Zhao Paul M Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of ScienceKunming 650201YunnanPeople’s Republic of China School of Science Mae Fah Luang UniversityChiang Rai 57100Thailand Center of Excellence in Fungal Research Mae Fah Luang UniversityChiang Rai 57100Thailand East and Central Asia Regional Office World Agroforestry Centre(ICRAF)Kunming 650201YunnanPeople’s Republic of China Department of Biology Faculty of ScienceChiang Mai UniversityChiang Mai 50200Thailand Mushroom Research Foundation 128 M.3 Ban Pa Deng T.Pa PaeA.Mae TaengChiang Mai 50150Thailand Department of Plant Medicine National Chiayi University300 Syuefu RoadChiayi City 60004TaiwanPeople’s Republic of China Department of Health Sciences Faculty of ScienceUniversity of MauritiusReduitMauritius Formerly Department of Botany Goa UniversityGoaIndia No.128/1-J Azad Housing SocietyCurcaGoa Velha 403108India Department of Crop Sciences College of Agricultural and Marine SciencesSultan Qaboos UniversityPO Box 8123 Al KhoudOman Sect.Environmental Sciences Dept.MeSVAUniversity of L’Aquila67100 CoppitoAQItaly Division of Food Technology Biotechnology&AgrochemistryCollege of Agriculture&Life SciencesChonnam National UniversityGwangju 61186Korea Fungal Resources Research Division NNIBRSangju-si 37242Korea Westerdijk Fungal Biodiversity Institute Uppsalalaan 83584 CT UtrechtThe Netherlands Department of Biotechnology and Biomedicine DTUBioengineeringTechnical University of Denmark2800 Kongens LyngbyDenmark Botanischer Garten und Botanisches Museum Berlin Freie Universitat BerlinKonigin-Luise-StraßBe 6-814195 BerlinGermany Laboratorio de Botanica/Liquenologia Instituto de BiocienciasUniversidade Federal de Mato Grosso do SulAvenida Costa e Silva s/nBairro UniversitarioCampo GrandeMatto Grosso do Sul CEP 79070-900Brazil Departamento de Biociencias Universidade Federal de SergipeItabaianaSergipe CEP:49500-000Brazil Shenzhen Key Laboratory of Microbial

This article is the tenth series of the Fungal Diversity Notes,where 114 taxa distributed in three phyla,ten classes,30 orders and 53 families are described and *** described in the present study include one new family(*** in Dothideomycetes),five new genera(Caatingomyces,Cryptoschizotrema,Neoacladium,Paramassaria and Trochilispora)and 71 new species,(*** thailandica,Amniculicola aquatica,***,Angustimassarina sylvatica,Blackwellomyces lateris,Boubovia gelatinosa,Buellia viridula,Caatingomyces brasiliensis,Calophoma humuli,Camarosporidiella mori,Canalisporium dehongense,Cantharellus brunneopallidus,***,Castanediella meliponae,Coprinopsis psammophila,Cordyceps succavus,Cortinarius minusculus,***,Diaporthe italiana,***,Diatrypella delonicis,Dictyocheirospora aquadulcis,***,Digitodesmium chiangmaiense,Distoseptispora dehongensis,***,Dothiorella styphnolobii,Ellisembia aurea,Falciformispora aquatic,Fomitiporia carpinea,***,Grammothele aurantiaca,***,Hermatomyces bauhiniae,Jahnula queenslandica,Kamalomyces mangrovei,Lecidella yunnanensis,Micarea squamulosa,Muriphaeosphaeria angustifoliae,Neoacladium indicum,Neodidymelliopsis sambuci,Neosetophoma miscanthi,***,Nodulosphaeria aquilegiae,***,Paramassaria samaneae,Penicillium circulare,***,***-pumilae,***,***,Phaeoisaria siamensis,Phaeopoacea asparagicola,Phaeosphaeria penniseti,Plectocarpon galapagoense,Porina sorediata,Pseudoberkleasmium chiangmaiense,Pyrenochaetopsis sinensis,Rhizophydium koreanum,Russula prasina,Sporoschisma chiangraiense,Stigmatomyces chamaemyiae,***,***,***,***,Thysanorea uniseptata,Torula breviconidiophora,***,Trochilispora schefflerae and Vaginatispora palmae).Further,twelve new combinations(*** cryptotrema,Prolixandromyces australi,***,***,***,P.

关键词： 71 new taxa Ascomycota Basidiomycota Dothideomycetes Eurotiomycetes Lecanoromycetes Leotiomycetes Pezizomycetes Phylogeny Taxonomy

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Publish me and protect me: Personalized and flexible location privacy protection in mobile social networks 23

Publish me and protect me: Personalized and flexible locatio...

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23rd IEEE International Symposium on Quality of Service, IWQoS 2015

作者： Wu, Yao Peng, Hui Zhang, Xiaoying Chen, Hong Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781467371131

With the increasing proliferation of the Mobile Social Networks (MSN) and the Location Based Service (LBS), location privacy has attracted broad attention in recent years. Most researches have been done with the assumption that the server is untrusted and a trusted third party is introduced to protect the user location privacy when a user sends queries for the location service. In this paper, we reconsider this assumption and propose a Personalized and Flexible location privacy protection Model (PFM) based on user relationship strength. We conduct researches in the situation that the server is trusted while malicious users in the MSN can disguise as a friend to break location privacy. We present an entropic TF-IDF based approach to measure the bi-directional relationship strength and propose probability distribution based cloaking model to protect user location privacy. We thoroughly evaluate our methods based on Quality of Privacy (QoP) via real and synthetic data. © 2015 IEEE.

关键词： Location based services

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Pathological Brain Detection Based on Online Sequential Extreme Learning Machine

Pathological Brain Detection Based on Online Sequential Extr...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University State Key Lab of CAD & CG Zhejiang University Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Key Laboratory of Statistical information technology and data mining State Statistics Bureau Department of Electrical Engineering The City College of New YorkCUNY

Magnetic resonance imaging(MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography(CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies(WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine(OS-ELM) was trained to differentiate pathological brains from the healthy *** experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%,which suggested that our method is effective.

关键词： magnetic resonance imaging wavelet entropy online sequential extreme learning machine Classification pattern recognition

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RDF partitioning for scalable SPARQL query processing

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Frontiers of Computer Science 2015年第6期9卷 919-933页

作者： Xiaoyan WANG Tao YANG Jinchuan CHEN Long HE Xiaoyong DU School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University Beijing 100872 China Information Center Supreme People's Court Beijing 100745 China State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China

The volume of RDF data increases dramatically within recent years, while cloud computing platforms like Hadoop are supposed to be a good choice for processing queries over huge data sets for their wonderful scalability. Previous work on evaluating SPARQL queries with Hadoop mainly focus on reducing the number of joins through careful split of HDFS files and algorithms for generating Map/Reduce jobs. However, the way of partitioning RDF data could also affect system performance. Specifically, a good partitioning solution would greatly reduce or even to- tally avoid cross-node joins, and significantly cut down the cost in query evaluation. Based on HadoopDB, this work processes SPARQL queries in a hybrid architecture, where Map/Reduce takes charge of the computing tasks, and RDF query engines like RDF-3X store the data and execute join operations. According to the analysis of query workloads, this work proposes a novel algorithm for automatically parti- tioning RDF data and an approximate solution to physically place the partitions in order to reduce data redundancy. It also discusses how to make a good trade-off between query evaluation efficiency and data redundancy. All of these pro- posed approaches have been evaluated by extensive experiments over large RDF data sets.

关键词： RDF data data partitioning SPARQL query

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Building extraction from panchromatic high-resolution remotely sensed imagery based on potential histogram and neighborhood Total variation

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Earth Science Informatics 2016年第4期9卷 497-509页

作者： Wenzao Shi Zhengyuan Mao Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian Normal University Fuzhou China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou China

In order to extract buildings using only gray information, this article proposed an approach for recognizing and extracting buildings from panchromatic high-resolution remotely sensed imagery based on shadows and segmentation. First, shadows were detected by potential histogram function. Second, the value of neighborhood total variation for each pixel was calculated, and then binarization and annotation were implemented to generate lable regions whose centroids were used as the seeds of the region growing segmentation, candidate buildings were selected from the segmentation result with the constraint of aspect ratio and rectangularity. At last, shadows were processed with open, dilate and corrode operations respectively, buildings were extracted by computing the adjacency relationship of the processed shadows and candidate buildings, and the building boundaries were fitted with the minimum enclosing rectangle. For verifying the validity of the proposed method, eighteen representative sub-images were chosen from PLEIADES images covering Shenzhen, China. Experimental results show that the average precision and recall of the proposed method are 97.95 % and 79.40 % for the object-based evaluation, and are 98.75 % and 83.16 % for the area-based evaluation respectively, and it has more 10 % and 6 % increase in the overall performance for above two evaluation criterion comparing with two other similar methods.

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Speech emotion recognition with acoustic and lexical features 40

Speech emotion recognition with acoustic and lexical feature...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Jin, Qin Li, Chengxin Chen, Shizhe Wu, Huimin Computer Science Department School of Information Renmin University of China Beijing China Key Lab of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing China

ISBN: (纸本)9781467369978

In this paper we explore one of the key aspects in building an emotion recognition system: generating suitable feature representations. We generate feature representations from both acoustic and lexical levels. At the acoustic level, we first extract low-level features such as intensity, F0, jitter, shimmer and spectral contours etc. We then generate different acoustic feature representations based on these low-level features, including statistics over these features, a new representation derived from a set of low-level acoustic codewords, and a new representation from Gaussian Supervectors. At the lexical level, we propose a new feature representation named emotion vector (eVector). We also use the traditional Bag-of-Words (BoW) feature. We apply these feature representations for emotion recognition and compare their performance on the USC-IEMOCAP database. We also combine these different feature representations via early fusion and late fusion. Our experimental results show that late fusion of both acoustic and lexical features achieves four-class emotion recognition accuracy of 69.2%. © 2015 IEEE.

关键词： Acoustic features Emotion lexicon Emotion recognition Lexical features Support vector machine

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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Publish me and protect me: Personalized and flexible location privacy protection in mobile social networks

Publish me and protect me: Personalized and flexible locatio...

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International Workshop on Quality of Service

作者： Yao Wu Hui Peng Xiaoying Zhang Hong Chen Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing China

关键词： Legged locomotion Weight measurement

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Sub-graph matching-based building changes detection using high-resolution remote sensing images

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International Journal of Earth Sciences and engineering 2016年第5期9卷 2172-2181页

作者： Huang, Feng-Hua Mao, Zheng-Yuan Shi, Wen-Zao Fuzhou University Fuzhou350116 China Yango College Fuzhou350015 China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou350002 China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou350002 China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou350002 China

High-resolution remote sensing (HRRS) images of urban regions have large viewing angle variations, significant noise jamming, and obvious building shadows. Hence, deviation and distortion usually occur to the buildings in HRRS images collected at different phases (multi-temporal HRRS images). The traditional detection methodology is ineffective for accurate and efficient building changes detection in multi-temporal HRRS images. In order to address these problems, this paper proposes a sub-graph matching-based building changes detection (SGMBCD) scheme. First, this paper presents a Graphcut-based buildings extracting (GCBE) method from multi-temporal HRRS images. Next, a sub-graph matching-based registration (SGMR) method is devised to register the previously extracted buildings from multi-temporal HRRS images and to obtain matched ASIFT feature point pairs and singular points. Finally, singular points and overlay analysis-based method (SPOA) is developed to detect building changes in multi-temporal HRRS images. The types of building changes included in this paper are changes (e.g., erection, dismantling, repairing, and reconstruction) and non-changes. In order to demonstrate the effectiveness of the proposed SGMBCD scheme, it is compared with five typical algorithms (i.e., BCDBPM, SDBBCD, BCDBICO, NCUTBCD, and RSMBCD) on three sets of multi-temporal WorldView2 test images. Experimental results show that in comparison with the other methods, SGMBCD can effectively address the challenging problem of building changes detection in multi-temporal HRRS images. The average recall ratio, precision ratio and F value is 91.47%, 86.49% and 88.91% respectively, and the average time consumption is 60.3 s. This demonstrates SGMBCD can detect building changes in multi-temporal HRRS images accurately and efficiently. © 2016 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

关键词： Buildings

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