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Semi-automatic classification method for mapping the rice-planted areas of Japan using multi-temporal Landsat images

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Remote Sensing Applications: Society and Environment 2018年 10卷 7-17页

作者： Sakamoto, Toshihiro Sprague, David S. Okamoto, Katsuo Ishitsuka, Naoki Institute for Agro-Environmental Sciences NARO Division of Informatics and Inventory 3-1-3 Kannondai Tsukuba 305-8604 Ibaraki Japan

The rice-planted areas of Japan have decreased continually with reduced rice consumption in the country. Remote sensing techniques are indispensable for providing up-to-date, public data for surveying the locations of paddy fields that have been abandoned or converted to upland crop farming. This study proposes a simple method, using multi-temporal Landsat data, for determining the threshold water index to consistently monitor the spatial distribution of the rice-planted area of Japan without using complex interactive manual operations. This method is much simpler compared to previous methods that semi-automatically or automatically determined the threshold water index based on statistical analyses using manually selected training data or the difference in histogram distribution information between flooded and non-flooded pixels. After comparing six water indices, the Normalized Difference Water Index calculated from green and short-wave infrared reflectance performed best when identifying the rice-planted area with the lowest estimation error (RMSE = 203 ha). It was also found that the proposed method had a higher sensitivity to small-size paddy fields than the previous methods, especially when using Landsat data that included many narrow rice-planted areas distributed along the valley regions. The proposed technique enabled us to consistently check the state of utilization of paddy fields across multiple Landsat images covering large regions of Japan. It was confirmed that the proposed method could accurately guide us to paddy fields that had been abandoned or were being used for upland crop farming, which is not possible from other available land use data, such as the National Land Numerical Information issued by the government. © 2018 Elsevier B.V.

关键词： classification algorithm Land use/land cover Phenology Rice crop Water index

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Chinese Text classification Review 9

Chinese Text Classification Review

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9th International Conference on Information Technology in Medicine and Education (ITME)

作者： Ma, Yajing Li, Yonghong Zhang, Xiang Wu, Xiaolong Northwest Minzu Univ Minist Educ Key Lab Chinas Ethn Languages & Informat Technol Lanzhou 730000 Gansu Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250300 Shandong Peoples R China

ISBN: (纸本)9781538677438

Text classification technology refers to the process of supervised learning of specified texts based on fixed rules. This paper describes the development process of text classification and its method techniques, including text representation, feature selection and classification algorithms, and draws the basic ideas, advantages and disadvantages of several current mainstream classification techniques.

关键词： Text classification Feature selection Weight calculation classification algorithm

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A Review of Crop classification Using Satellite-Based Polarimetric SAR Imagery 7

A Review of Crop Classification Using Satellite-Based Polari...

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7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Sun, Zheng Wang, Di Zhong, Geji Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Beijing Peoples R China

ISBN: (纸本)9781538650394

Crop area information can provide decision support for agricultural production management, and it is an important basis for formulating grain policy and economic plan. Remote sensing has an irreplaceable role in large-scale agricultural monitoring, in which optical remote sensing is greatly influenced by cloud and rain weather. It is often unable to obtain clear and clear optical remote sensing images in the critical period of crop growth, which seriously images the accuracy and timeliness of the classification of crop remote sensing. Synthetic aperture radar (SAR) capable of monitoring ground objects throughout the day and all weather, Polarimetric SAR data also include scattering matrix, geometric details and permittivity information, it is sensitive to the geometry and height of vegetation, which can make up for the lack of optical remote sensing and has unique advantages in crop identification and monitoring. So full polarization SAR has wide application demand and great potential in crop remote sensing monitoring. The effective use of full polarimetric SAR data to classify crops is of great academic and practical value for promoting the greater role of radar technology in national agricultural remote sensing monitoring and agricultural supply side structural reform. This paper summarizes the different data types and polarization decomposition methods of polarization SAR used in crop identification. According to the current research, there are the following deficiencies: first, most of the research subjects are rice, and dry land crops are less studied. Secondly, the identification of dry land crops is not accurate enough, and the average recognition accuracy is less than 85%. Finally, there are few researches on the scattering mechanism, which leads to poor rationality and poor universality. First, determine the scattering mechanism of dry land crops, and use scattering mechanism to improve the accuracy and universality of remote sensing recognition of dryland

关键词： synthetic aperture radar crop classification full polarization classification algorithm

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A radiosity-based method to avoid calibration for indoor positioning systems

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 105卷 89-101页

作者： Belmonte-Fernandez, Oscar Montoliu, Raul Torres-Sospedra, Joaquin Sansano-Sansano, Emilio Chia-Aguilar, Daniel Jaume I Univ Inst New Imaging Technol Castellon De La Plana 12071 Spain

Due to the widespread use of mobile devices, services based on the users current indoor location are growing in significance. Such services are developed in the Machine Learning and Experst Systems realm, and ranges from guidance for blind people to mobile tourism and indoor shopping. One of the most used techniques for indoor positioning is WiFi fingerprinting, being its use of widespread WiFi signals one of the main reasons for its popularity, mostly on high populated urban areas. Most issues of this approach rely on the data acquisition phase;to manually sample WiFi RSSI signals in order to create a WiFi radio map is a high time consuming task, also subject to re-calibrations, because any change in the environment might affect the signal propagation, and therefore degrade the performance of the positioning system. The work presented in this paper aims at substituting the manual data acquisition phase by directly calculating the WiFi radio map by means of a radiosity signal propagation model. The time needed to acquire the WiFi radio map by means of the radiosity model dramatically reduces from hours to minutes when compared with manual acquisition. The proposed method is able to produce competitive results, in terms of accuracy, when compared with manual sampling, which can help domain experts develop services based on location faster. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Indoor positioning Radiosity classification algorithm Machine learning

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A survey on dynamic mobile malware detection

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SOFTWARE QUALITY JOURNAL 2018年第3期26卷 891-919页

作者： Yan, Ping Yan, Zheng Xidian Univ Sch Cyber Engn State Key Lab Integrated Serv Networks Xian 710071 Shaanxi Peoples R China Aalto Univ Dept Commun & Networking Espoo 02150 Finland

The outstanding advances of mobile devices stimulate their wide usage. Since mobile devices are coupled with third-party applications, lots of security and privacy problems are induced. However, current mobile malware detection and analysis technologies are still imperfect, ineffective, and incomprehensive. Due to the specific characteristics of mobile devices such as limited resources, constant network connectivity, user activities and location sensing, and local communication capability, mobile malware detection faces new challenges, especially on dynamic runtime malware detection. Many intrusions or attacks could happen after a mobile app is installed or executed. The literature still expects practical and effective dynamic malware detection approaches. In this paper, we give a thorough survey on dynamic mobile malware detection. We first introduce the definition, evolution, classification, and security threats of mobile malware. Then, we summarize a number of criteria and performance evaluation measures of mobile malware detection. Furthermore, we compare, analyze, and comment on existing mobile malware detection methods proposed in recent years based on evaluation criteria and measures. Finally, we figure out open issues in this research field and motivate future research directions.

关键词： Mobile malware Dynamic malware detection Security threats classification algorithm Evaluation criteria

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Toward Energy-Awareness Smart Building: Discover the Fingerprint of Your Electrical Appliances

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第4期14卷 1458-1468页

作者： Ma, Meng Lin, Weilan Zhang, Jingbin Wang, Ping Zhou, Yuchen Liang, Xiaoxing Peking Univ Sch Elect Engn & Comp Sci Beijing 100871 Peoples R China Peking Univ Sch Software & Microelect Beijing 102600 Peoples R China Peking Univ Natl Engn Res Ctr Software Engn Beijing 100871 Peoples R China Peking Univ Sch Software & Microelect Beijing 100871 Peoples R China Minist Educ Key Lab High Confidence Software Technol PKU Beijing 100871 Peoples R China IBM Res China Beijing 100094 Peoples R China

Energy efficiency raises significant concerns as it is one of the most promising ways to mitigate climate change. Disaggregation and identification of individual electrical appliances activities are one of the essentials for energy preservation especially for smart buildings. This paper proposes a lightweight electrical appliance activity detection approach for smart building, which leverages a single smart metering device to establish a learning and detection processing for multiple appliances. In this system, data interpolation and transition detection algorithm are proposed to effectively reduce the cost of model training and optimize the detection accuracy. The concept of appliance fingerprint is proposed and a variety of fingerprints, including appliance-based and context-based, are defined to depict fine-grained appliance characteristics. Based on these fingerprints, the paper proposes a multisource fingerprint-weighting KNN (FWKNN) classification algorithm and presents a boosting framework for continuous online learning and detection. A prototype system is implemented and demonstrated in IBM Bluemix PaaS cloud platform. Experimental result and analysis prove that FWKNN outperforms other benchmark methods in detection accuracy.

关键词： Activity learning appliance load monitoring (ALM) classification algorithm fingerprint extraction Internet of Things smart building

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Inferring ecological processes from population signatures: A simulation-based heuristic for the selection of sampling strategies

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ECOLOGICAL MODELLING 2018年 385卷 12-25页

作者： Bellot, Benoit Poggi, Sylvain Baudry, Jacques Bourhis, Yoann Parisey, Nicolas INRA UMR IGEPP 1349 Domaine MotteBP 35327 F-35653 Le Rheu France INRA UMR BAGAP 65 Rue St BrieucCS 84215 F-35042 Rennes France Rothamsted Res Dept Biointeract & Crop Protect Harpenden AL5 2JQ Herts England

A good knowledge about species traits variability in relation to their environment is the cornerstone of landscape-oriented species management studies. One way to infer this relationship is to compare species signatures in space and time from field data with spatially explicit population dynamics models outputs. However, the inference robustness relies on the available field data, and thus on the quality of the underlying sampling strategy. Field sampling is constrained by several factors, such as the number of landscape replicates, possible number of temporal sessions and number of sample locations, that need to be accounted for prior to field sampling. We set and illustrate a heuristic method to answer the question of optimal sampling conditioned by these landscape-induced constraints. First we studied a real agricultural landscape to determine its mean properties in terms of configuration and composition. The real landscape properties were used as constraints in a landscape model to generate a collection of landscapes with similar properties. On the other hand, we formulated population dynamics models (hereafter noted Process Models (PM)) carrying competing hypotheses about two ecological processes-population growth and dispersal-in relation to spatial covariates for Pterostichus melanarius, a carabid species involved in pest regulation. We simulated these spatially explicit models and extracted their sampling-dependent signatures, i.e. metrics computed on different population samples. We defined a sampling design quality as its ability to capture the contrasts between the PM signatures, summarised by the performance of a classification procedure. The most relevant sampling design was selected on the basis of classification performance and in situ feasibility. Finally we explored the effects of the a priori ecological hypotheses quality on classification performances, through a sensitivity analysis of the PM parameters. While some improvements remain to be achiev

关键词： Ecological traits Reaction-diffusion models Population patterns Sampling designs classification algorithm Landscape ecology

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Trend Detection for the Extent of Irrigated Agriculture in Idaho's Snake River Plain, 1984-2016

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REMOTE SENSING 2018年第1期10卷 145-145页

作者： Chance, Eric W. Cobourn, Kelly M. Thomas, Valerie A. Virginia Polytech Inst & State Univ Dept Forest Resources & Environm Conservat 310 West Campus Dr Blacksburg VA 24061 USA

Understanding irrigator responses to changes in water availability is critical for building strategies to support effective management of water resources. Using remote sensing data, we examine farmer responses to seasonal changes in water availability in Idaho's Snake River Plain for the time series 1984-2016. We apply a binary threshold based on the seasonal maximum of the Normalized Difference Moisture Index (NDMI) using Landsat 5-8 images to distinguish irrigated from non-irrigated lands. We find that the NDMI of irrigated lands increased over time, consistent with trends in irrigation technology adoption and increased crop productivity. By combining remote sensing data with geospatial data describing water rights for irrigation, we show that the trend in NDMI is not universal, but differs by farm size and water source. Farmers with small farms that rely on surface water are more likely than average to have a large contraction (over -25%) in irrigated area over the 33-year period of record. In contrast, those with large farms and access to groundwater are more likely than average to have a large expansion (over +25%) in irrigated area over the same period.

关键词： agriculture classification algorithm farm size groundwater irrigation technology surface water water rights

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Landscape functional zoning at a county level based on ecosystem services bundle: Methods comparison and management indication

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2019年 249卷 109315-000页

作者： Liu, Yanxu Li, Tong Zhao, Wenwu Wang, Shuai Fu, Bojie Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol Beijing 100875 Peoples R China

The perspective of ecosystem services bundle is virtually a spatial clustering on landscape to mapping the relationship between ecosystem services and support the spatial strategy of landscape management. However, the efficiency of various clustering algorithms for geographically different regions are still in obscurity. In this study, we provided landscape functional zoning as a planning tool based on the ecosystem services bundles formed by carbon sequestration, soil retention and water yield. Then we used four landscape pattern indices to evaluate the performance of six clustering algorithms on landscape functional zoning. The case counties include Lankao, Jinggangshan and Luquan in China. The results showed the Natural Breaks (Jenks) scheme should be the most reasonable zone because of its high aggregated distribution and low diversity. This scheme was adjusted using some other schemes and has been employed as the final 7 kinds of zoning types. There were 5 types appeared in Lankao and Jinggangshan, and 6 types appeared in Luquan. We discussed that landscape functional zone can be a nexus connecting landscape planning and social policy. Rural reconstructing process on landscape was depicted, and landscape functional zone was proposed a practical planning tool bridged human wellbeing. The task of landscape functional zoning with the management indications may provide interdisciplinary support to decision-makers and natural resource users on landscape management.

关键词： Landscape functional zone Ecosystem services classification algorithm County level Rural restructuring

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Oil source recognition technology using concentration-synchronous-matrix-fluorescence spectroscopy combined with 2D wavelet packet and probabilistic neural network

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SCIENCE OF THE TOTAL ENVIRONMENT 2018年 616卷 632-638页

作者： Huang, Xiao-Dong Wang, Chun-Yan Fan, Xin-Min Zhang, Jin-Liang Yang, Chun Wang, Zhen-Di Weifang Univ Dept Phys & Elect Sci Weifang 261061 Peoples R China Weifang Univ Inst New Electromagnet Mat Weifang 261061 Peoples R China Beijing Normal Univ Coll Resources Sci & Technol Beijing 100875 Peoples R China Environm Canada Sci & Technol Branch ESTS 335 River Rd Ottawa ON K1A 0H3 Canada

Developing an accurate, rapid and economic oil source recognition method is essential for water recourses protection. Concentration-synchronous-matrix-fluorescence (CSMF) spectroscopy combined with 2D wavelet packet and probabilistic neural network (PNN) was proposed for source recognition of crude oil and petroleum products samples in this study. 2D wavelet packet was used to extract wavelet packet coefficients as the feature vectors from CSMF contour image and four algorithms, Back-propagation (BP) neural network, Radial based function neural network (RBFNN), Support vector Machine (SVM) and probabilistic neural network (PNN) were carried out for pattern recognition. With the introduction of interference factors such as weathering and sea water adulteration to the three samples from Bohai bay territory of China, the comparison about accuracy and recognition time of the four methods was discussed and the results showed that PNN network maintain the highest recognition accuracy and speed. These findings may offer potential application for oil spill recognition for unconventional oil. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Oil spill Oil source recognition Concentration-synchronous-matrixfluorescence (CSMF) classification algorithm Weathering experiment

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