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Unsupervised Object-Based Change Detection via a Weibull Mixture Model-Based Binarization for High-Resolution Remote Sensing Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2018年第1期15卷 63-67页

作者： Wu, Tianjun Luo, Jiancheng Fang, Jianwu Ma, Jianghong Song, Xueli Changan Univ Coll Sci Dept Math & Informat Sci Xian 710064 Shaanxi Peoples R China Zhejiang Ocean Univ Key Lab Oceanog Big Data Min & Applicat Zhejiang Zhoushan 316022 Peoples R China Fuzhou Univ Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350002 Fujian Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Changan Univ Sch Elect & Control Engn Xian 710064 Shaanxi Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Shaanxi Peoples R China

Object-based change detection (CD) is an effective method of identifying detailed changes in land features by contrastively observing the same areas of high-resolution remote sensing images at different times. Binarization is the important step in partitioning changed and unchanged classes in the unsupervised domain. We formulate a novel binarization technique based on the Weibull mixture model, where generated similarity measure images are modeled using a mixture of nonnormal Weibull distributions. The parameters in the model are further globally estimated by employing a genetic algorithm. Two data sets with high-resolution remote sensing images are used to evaluate the effectiveness of the proposed method. Experimental results demonstrate that the method allows better and more robust unsupervised object-based CD than do state-of-the-art threshold-based and clustering-based methods. Advantages of the proposed method are embodied in the modeling of relatively few data of the changed class with a skewed and long tail distribution.

关键词： Binarization genetic algorithm (GA) unsupervised object-based change detection (UOBCD) Weibull mixture model (WMM)

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Three foci at the science-policy interface for systemic Sustainable Development Goal acceleration

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NATURE COMMUNICATIONS 2024年第1期15卷 8600页

作者： Pradhan, Prajal Weitz, Nina Daioglou, Vassilis Abrahao, Gabriel M. Allen, Cameron Ambrosio, Geanderson Arp, Frederike Asif, Furqan Bennich, Therese Benton, Tim G. Biermann, Frank Cao, min Carlsen, Henrik Chen, Fang Chen, min Daams, Michiel N. Dawes, Jonathan H. P. Dhakal, Shobhakar Gilmore, Elisabeth Miguel, Luis J. Hubacek, Klaus Hu, Yuanchao Jager, Wander Samir, K. C. Kearney, Norman M. Khot, Utkarsh Ashok Kluck, Teun Kulkarni, Shridhar Leininger, Julia Li, Chaohui Li, Jing Lotze-Campen, Hermann Parrado-Hernando, Gonzalo Pedercini, Matteo Phuyal, RamKumar Prell, Christina Rijal, Arpan Schweizer, Vanessa Sijtsma, Frans J. Soergel, Bjoern Spittler, Nathalie van Vuuren, Detlef Warchold, Anne Weber, Eartha Wicke, Birka Widerberg, Oscar Wilts, Rienne Wingens, Christopher Wu, Chaoyang Xing, Qiang Yan, Jin Yuan, Zifeng Zhou, Xin Zimm, Caroline Univ Groningen Energy & Sustainabil Res Inst Groningen ESRIG Integrated Res Energy Environm & Soc IREES Groningen Netherlands Leibniz Assoc Potsdam Inst Climate Impact Res PIK Potsdam Germany Stockholm Environm Inst Stockholm Sweden Univ Utrecht Copernicus Inst Sustainable Dev Utrecht Netherlands PBL Netherlands Environm Assessment Agcy The Hague Netherlands Monash Univ Monash Sustainable Dev Inst Melbourne Australia Aalborg Univ Ctr Blue Governance Aalborg Denmark Chatham House London England Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ PRC Nanjing Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Univ Groningen Fac Spatial Sci Dept Econ Geog Groningen Netherlands Univ Groningen Rudolf Agr Sch Sustainable Dev Groningen Netherlands Univ Bath Dept Math Sci Bath England Asian Inst Technol Dept Energy & Climate Change Pathum Thani Thailand Carleton Univ Civil & Environm Engn Ottawa ON Canada Univ Valladolid Sch Ind Engn Dept Syst Engn & Automat Grp Energy Econ & Syst Dynam Valladolid Spain Wuhan Univ Sch Resource & Environm Sci Wuhan Peoples R China Univ Groningen Groningen Ctr Social Complex Studies Groningen Netherlands Int Inst Appl Syst Anal IIASA Laxenburg Austria Populat & Stastist Res Hub Lalitpur Nepal Univ Bern Ctr Dev & Environm Bern Switzerland German Inst Dev & Sustainabil IDOS Bonn Germany Peking Univ Coll Engn Lab Syst Ecol & Sustainabil Sci Beijing Peoples R China Humboldt Univ Dept Agr Econ Berlin Germany Millennium Inst Washington DC USA Govt Nepal Natl Planning Commiss Kathmandu Nepal Univ Groningen Fac Spatial Sci Groningen NL Netherlands Univ Groningen Fac Econ & Business Dept Operat Groningen Netherlands Univ Waterloo Dept Knowledge Integrat Waterloo ON Canada BOKU Univ Univ Nat Resources & Life Sci Vienna Austria

The integrated and indivisible nature of the SDGs is facing implementation challenges due to the silo approaches. We present the three interconnected foci (SDG interactions, modeling, and tools) at the science-policy interface to address these challenges. Accounting for them will support accelerated SDG progress, operationalizing the integration and indivisibility principles.

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Overlapping Community Discovery Based on the Combination of Node Influence and β-Connected Neighbors

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intERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS 2019年第4期28卷

作者： Chen, Rongwang Wu, Qingshou Guo, Wenzhong Guo, Kun Wang, Qinze Wuyi Univ Coll Math & Comp Sci Wuyishan 354300 Fujian Peoples R China Digital Fujian Tourism Big Data Inst Wuyishan 354300 Fujian Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Fujian Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou 350108 Fujian Peoples R China Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350108 Fujian Peoples R China

We propose an overlapping community discovery algorithm that combines node influence and beta-connected neighbors for effectively detecting the overlapping community structure of complex networks. On the basis of the node influence and beta-connected neighbors, our method accurately detects the core node community and uses the improved similarity between the node and community to expand the core node community. Accordingly, the discovery and optimization of network overlapping communities are realized. Experiments on artificial and real-world networks demonstrate that our method significantly and consistently outperforms other comparison methods.

关键词： Community discovery overlapping community node influence beta-connected neighbors

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A Novel Texture Generation Super Resolution Model 7

A Novel Texture Generation Super Resolution Model

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7th international Conference on Information Technology and Quantitative Management (ITQM) - Information Technology and Quantitative Management Based on Artificial intelligence

作者： Li, Biao Shi, Yong Li, Sujuan Wang, Bo Qi, Zhiquan Liu, Jiabin Univ Chinese Acad Sci Sch Econ & Management Beijing 101408 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Zhengzhou Univ Dept Otol Affiliated Hosp 1 Zhengzhou 450052 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

Recently, super-resolution methods pursue visual pleasant details attract more attention in academic circle. Unlike former accurate driving models, they leverage new losses measured the difference of features extracting from a pre-trained deep learning model. Moreover, the popular GANs structure are introduced to approach better outputs. In general, these new visual feeling promoting losses are constrained by the learning ability of model and effected by the weakness of GANs. In order to address these problems, we propose a novel SR method to generating abundant details. Based on former SR methods and texture generated models, we combine new model constructure and integrated loss function to achieve our goal. The new constructure derives from the very thought that more information from the LR input leads to more accurate results. The new integrated loss try to decrease the weakness of each one by corresponding loss.. Experiments show that our model achieves higher quality generations than former methods. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Super Resolution Multi Scale Smooth Loss Visual Quality

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A CRISPR-Cas12a-based specific enhancer for more sensitive detection of SARS-CoV-2 infection

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EBIOMEDICINE 2020年 61卷 103036-103036页

作者： Huang, Weiren Yu, Lei Wen, Donghua Wei, Dong Sun, Yangyang Zhao, Huailong Ye, Yu Chen, Wei Zhu, Yongqiang Wang, Lijun Wang, Li Wu, Wenjuan Zhao, Qianqian Xu, Yong Gu, Dayong Nie, Guohui Zhu, Dongyi Guo, Zhongliang Ma, Xiaoling Niu, Liman Huang, Yikun Liu, Yuchen Peng, Bo Zhang, Renli Zhang, Xiuming Li, Dechang Liu, Yang Yang, Guoliang Liu, Lanzheng Zhou, Yunying Wang, Yunshan Hou, Tieying Gao, Qiuping Li, Wujiao Chen, Shuo Hu, Xuejiao Han, Mei Zheng, Huajun Weng, Jianping Cai, Zhiming Zhang, Xinxin Song, Fei Zhao, Guoping Wang, Jin Shenzhen Univ Shenzhen Univ Sch Med Shenzhen Peoples Hosp 2 Int Canc CtrAffiliated Hosp 1Dept Urol Shenzhen 518039 Peoples R China Tongji Univ Shanghai East Hosp Dept Lab Med Sch Med Shanghai 200123 Peoples R China Shanghai Jiao Tong Univ Ruijin Hosp Res Lab Clin Virol Sch Med Shanghai 200025 Peoples R China Jinan Ctr Dis Control & Prevent Jinan 250021 Shandong Peoples R China Chinese Acad Sci Shenzhen Inst Synthet Biol Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Chinese Natl Human Genome Ctr Shanghai Shanghai Most Key Lab Hlth & Dis Genom Shanghai 201203 Peoples R China Shandong Univ Jinan Infect Dis Hosp Jinan 250021 Shandong Peoples R China Shandong Univ Med Res & Lab Diagnost Ctr Jinan Cent Hosp Jinan 250013 Shandong Peoples R China Univ Sci & Technol China Affiliated Hosp USTC 1 Div Life Sci & Med Hefei 230001 Anhui Peoples R China Shenzhen Ctr Dis Control & Prevent Shenzhen 518055 Peoples R China Huazhong Univ Sci & Technol Shenzhen Peoples Hosp 6 Nanshan Hosp Union Shenzhen Hosp Shenzhen 518052 Peoples R China Yuebei Second Peoples Hosp Shaoguan 512000 Guangdong Peoples R China Shanghai Inst Qual Inspect & Tech Res Natl Qual Supervis & Inspect Ctr Food Prod Shangh Shanghai 200233 Peoples R China Prov Peoples Hosp Guangdong Acad Med Sci Guangzhou Lab Med Guangzhou 510080 Guangdong Peoples R China Tolo Biotechnol Co Ltd Shanghai 200233 Peoples R China Publ Hlth Med Ctr Chongqing Municipal Chongqing 400036 Peoples R China Chinese Acad Sci CAS Key Lab Synthet Biol Inst Plant Physiol & Ecol Shanghai Inst Biol Sci Shanghai 200032 Peoples R China Chinese Acad Sci CAS MPG Partner Inst Computat Biol Key Lab Computat Biol Biomed Big Data CtrShanghai Inst Nutr & Hlth Shanghai 200031 Peoples R China Shanghai Normal Univ Coll Life Sci Shanghai 200234 Peoples R China

Background: Real-time reverse transcription-PCR (rRT-PCR) has been the most effective and widely implemented diagnostic technology since the beginning of the COVID-19 pandemic. However, fuzzy rRT-PCR readouts with high Ct values are frequently encountered, resulting in uncertainty in diagnosis. Methods: A Specific Enhancer for PCR-amplified Nucleic Acid (SENA) was developed based on the Cas12a transcleavage activity, which is specifically triggered by the rRT-PCR amplicons of the SARS-CoV-2 Orf1ab (O) and N fragments. SENA was first characterized to determine its sensitivity and specificity, using a systematic titration experiment with pure SARS-CoV-2 RNA standards, and was then verified in several hospitals, employing a couple of commercial rRT-PCR kits and testing various clinical specimens under different scenarios. Findings: The ratio (10 min/5 min) of fluorescence change (FC) with mixed SENA reaction (mix-FCratio) was defined for quantitative analysis of target O and N genes, and the Limit of Detection (LoD) of mix-FCratio with 95% confidence interval was 1.2 <= 1.6 <= 2.1. Totally, 295 clinical specimens were analyzed, among which 21 uncertain rRT-PCR cases as well as 4 false negative and 2 false positive samples were characterized by SENA and further verified by next-generation sequencing (NGS). The cut-off values for mix-FCratio were determined as 1.145 for positive and 1.068 for negative. interpretation: SENA increases both the sensitivity and the specificity of rRT-PCR, solving the uncertainty problem in COVID-19 diagnosis and thus providing a simple and low-cost companion diagnosis for combating the pandemic. (C) 2020 The Author(s). Published by Elsevier B.V.

关键词： COVID-19 SARS-CoV-2 rRT-PCR CRISPR diagnosis Cas12a SENA

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Learning from label proportions on high-dimensional data

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NEURAL NETWORKS 2018年 103卷 9-18页

作者： Shi, Yong Liu, Jiabin Qi, Zhiquan Wang, Bo Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100190 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Learning from label proportions (LLP), in which the training data is in the form of bags and only the proportion of each class in each bag is available, has attracted wide interest in machine learning. However, how to solve high-dimensional LLP problem is still a challenging task. In this paper, we propose a novel algorithm called learning from label proportions based on random forests (LLP-RF), which has the advantage of dealing with high-dimensional LLP problem. First, by defining the hidden class labels inside target bags as random variables, we formulate a robust loss function based on random forests and take the corresponding proportion information into LLP-RF by penalizing the difference between the ground truth and estimated label proportion. Second, a simple but efficient alternating annealing method is employed to solve the corresponding optimization model. At last, various experiments demonstrate that our algorithm can obtain the best accuracies on high-dimensional data compared with several recently developed methods. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Optimization High-dimensional data Learning from label proportions (LLP) Random forests

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Learning With label Proportions via NPSVM

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IEEE TRANSACTIONS ON CYBERNETICS 2017年第10期47卷 3293-3305页

作者： Qi, Zhiquan Wang, Bo Meng, Fan Niu, Lingfeng Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China

Recently, learning from label proportions (LLPs), which seeks generalized instance-level predictors merely based on bag-level label proportions, has attracted widespread interest. However, due to its weak label scenario, LLP usually falls into a transductive learning framework accounting for an intractable combinatorial optimization issue. In this paper, we propose a brand new algorithm, called LLPs via nonparallel support vector machine (LLP-NPSVM), to facilitate this dilemma. To harness satisfactory data adaption, instead of transductive learning fashion, our scheme determined instance labels according to two nonparallel hyper-planes under the supervision of label proportion information. In a geometrical view, our approach can be interpreted as an alternative competitive method benefiting from large margin clustering. In practice, LLP-NPSVM can be efficiently addressed by applying two fast sequential minimal optimization paths iteratively. To rationally support the effectiveness of our method, finite termination and monotonic decrease of the proposed LLP-NPSVM procedure were essentially analyzed. Various experiments demonstrated our algorithm enjoys rapid convergence and robust numerical stability, along with best accuracies among several recently developed methods in most cases.

关键词： k-plane clustering learning with label proportions (LLPs) nonparallel support vector machine (NPSVM)

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Land Use Change and Climate Variation in the Three Gorges Reservoir Catchment from 2000 to 2015 Based on the Google Earth Engine

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SENSORS 2019年第9期19卷 2118.-2118.页

作者： Hao, Binfei Ma, mingguo Li, Shiwei Li, Qiuping Hao, Dalei Huang, Jing Ge, Zhongxi Yang, Hong Han, Xujun Southwest Univ Sch Geog Sci Chongqing Key Lab Karst Environm Res Base Karst Ecoenvironm Nanchuan ChongqingMin Chongqing 400715 Peoples R China Southwest Univ Chongqing Engn Res Ctr Remote Sensing Big Data Ap Sch Geog Sci Chongqing 400715 Peoples R China Southwest Univ Chongqing Jinfo Mt Field Sci Observat & Res Stn K Sch Geog Sci Chongqing 400715 Peoples R China Shang Zheng Beijing Informat Technol Co Ltd Beijing 100086 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci Beijing 100101 Peoples R China Univ Reading Dept Geog & Environm Sci Reading RG6 6AB Berks England

Possible environmental change and ecosystem degradation have received increasing attention since the construction of Three Gorges Reservoir Catchment (TGRC) in China. The advanced Google Earth Engine (GEE) cloud-based platform and the large number of Geosciences and Remote Sensing datasets archived in GEE were used to analyze the land use and land cover change (LULCC) and climate variation in TGRC. GlobeLand30 data were used to evaluate the spatial land dynamics from 2000 to 2010 and Landsat 8 Operational Land Imager (OLI) images were applied for land use in 2015. The interannual variations in the Land Surface Temperature (LST) and seasonally integrated normalized difference vegetation index (SINDVI) were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) products. The climate factors including air temperature, precipitation and evapotranspiration were investigated based on the data from the Global Land data Assimilation System (GLDAS). The results indicated that from 2000 to 2015, the cultivated land and grassland decreased by 2.05% and 6.02%, while the forest, wetland, artificial surface, shrub land and waterbody increased by 3.64%, 0.94%, 0.87%, 1.17% and 1.45%, respectively. The SINDVI increased by 3.209 in the period of 2000-2015, while the LST decreased by 0.253 degrees C from 2001 to 2015. The LST showed an increasing trend primarily in urbanized area, with a decreasing trend mainly in forest area. In particular, chongqing City had the highest LST during the research period. A marked decrease in SINDVI occurred primarily in urbanized areas. Good vegetation areas were primarily located in the eastern part of the TGRC, such as Wuxi County, Wushan County, and Xingshan County. During the 2000-2015 period, the air temperature, precipitation and evapotranspiration rose by 0.0678 degrees C/a, 1.0844 mm/a, and 0.4105 mm/a, respectively. The climate change in the TGRC was influenced by LULCC, but the effect was limited. What is more, the climate cha

关键词： Land use and land cover change (LULCC) land surface temperature (LST) seasonally integrated normalized difference vegetation index (SINDVI) Climate change Three Gorges Reservoir Catchment (TGRC) Google Earth Engine

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Kernel Extreme Learning Machine for Learning from label Proportions 18th

Kernel Extreme Learning Machine for Learning from Label Prop...

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18th international Conference on Computational Science (ICCS)

作者： Yuan, Hao Wang, Bo Niu, Lingfeng Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Univ Int Business & Econ Sch Informat Technol & Management Beijing 100029 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

ISBN: (纸本)9783319937014;9783319937007

As far as we know, Inverse Extreme Learning Machine (IELM) is the first work extending ELM to LLP problem. Due to basing on extreme learning machine (ELM), it obtains the fast speed and achieves competitive classification accuracy compared with the existing LLP methods. Kernel extreme learning machine (KELM) generalizes basic ELM to the kernel-based framework. It not only solves the problem that the node number of the hidden layer in basic ELM depends on manual setting, but also presents better generalization ability and stability than basic ELM. However, there is no research based on KELM for LLP. In this paper, we apply KELM and design the novel method LLP-KELM for LLP. The classification accuracy is greatly improved compared with IELM. Lots of numerical experiments manifest the advantages of our novel method.

关键词： Learning from label proportions Extreme learning machine Kernel Classifier calibration

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Studying the Influence of Tourism Flow on Foreign Exchange Rate by IABC and Time-Series Models 13th

Studying the Influence of Tourism Flow on Foreign Exchange R...

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13th international Conference on intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP)

作者： Tsai, Pei-Wei Chen, Zhi-Sheng Xue, Xingsi Chang, Jui-Fang Swinburne Univ Technol Dept Comp Sci & Software Engn Hawthorn Vic 3122 Australia Natl Kaohsiung Univ Appl Sci Dept Int Business Kaohsiung 80708 Taiwan Fujian Univ Technol Coll Informat Sci & Engn Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China

ISBN: (纸本)9783319638560;9783319638553

In this study, we focus on analysing the relationship between the foreign exchange rate and the international tourism flow. Three foreign exchange rate forecasting models including GARCH(1,1), EGARCH(1,1), and the IABC forecasting model based on the computational intelligence are employed to produce the forecasting results. The Mean Absolute Percentage Error (MAPE) is selected to be the evaluation criterion for comparing the forecasting results of these models. The experiments contain the USD/NTD foreign exchange rate and the inbound international tourism flows in years of 2009 to 2010. The experimental results reveal that adding the international tourism flow as the new reference in the forecasting process has the positive contribution to the foreign exchange rate forecasting results.

关键词： GARCH EGARCH IABC Rate forecasting Tourism flow

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