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| 1 | Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm显示文摘There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery. | YU XianChuan HE Hui HU Dan ZHOU Wei | 2014 | Science China Earth Sciences2014，57，6： | 4 |
| 2 | The information content of rules and rule sets and its application显示文摘The information content of rules is categorized into inner mutual information con-tent and outer impartation information content. Actually, the conventional objective interestingness measures based on information theory are all inner mutual infor-mation, which represent the confidence of rules and the mutual information be-tween the antecedent and consequent. Moreover, almost all of these measures lose sight of the outer impartation information, which is conveyed to the user and help the user to make decisions. We put forward the viewpoint that the outer impartation information content of rules and rule sets can be represented by the relations from input universe to output universe. By binary relations, the interaction of rules in a rule set can be easily represented by operators: union and intersection. Based on the entropy of relations, the outer impartation information content of rules and rule sets are well measured. Then, the conditional information content of rules and rule sets, the independence of rules and rule sets and the inconsistent knowledge of rule sets are defined and measured. The properties of these new measures are discussed and some interesting results are proven, such as the information con-tent of a rule set may be bigger than the sum of the information content of rules in the rule set, and the conditional information content of rules may be negative. At last, the applications of these new measures are discussed. The new method for the appraisement of rule mining algorithm, and two rule pruning algorithms, λ-choice and RPCIC, are put forward. These new methods and algorithms have predominance in satisfying the need of more efficient decision information. | HU Dan LI HongXing YU XianChuan | 2008 | Science in China(Series F)2008，51，12： | 3 |
| 3 | Assessment and analysis of microwave emissivity and transmissivity of a deciduous forest towards the estimate of vegetation biomass显示文摘Forests play an important role in the global carbon cycle and have a potential impact on global climatic change.Monitoring forest biomass is of considerable importance in understanding the hydrological cycle.Because of the problem of dense forest cover,no reliable method with which to retrieve soil moisture in forest areas from the microwave emission signature has been established.All of these issues relate to the microwave emissivity and transmissivity characteristics of a forest.The microwave emission contribution received by a sensor above a forest canopy comes from both the soil surface and the vegetation layer.To analyze the relationship of forest biomass and forest emission and transmissivity,a high-order emission model,the matrix-doubling model,which consists of both soil and vegetation models,was developed and then validated for a young deciduous forest stand in a field experiment.To simulate the emissivity and transmissivity of a deciduous forest in the L and X bands using the matrix-doubling model,the parameters of components of deciduous trees when the leaf area index varies from 1 to10 were generated by an L-system and a forest growth model.The emissivity and transmissivity of a forest and the relationships of these parameters to forest biomass are presented and analyzed in this paper.Emissivity in the L band when the leaf area index is less than 6 and at viewing angles less than 40°,and transmissivity in the L band are the most sensitive parameters in deciduous forest biomass estimation. | ZHANG ZhongJun YU XianChuan ZHAO ShaoJie CHAI LinNa | 2014 | Science China Earth Sciences2014，57，3： | 2 |
| 4 | Novel fuzzy uncertainty modeling for land cover classification based on clustering analysis显示文摘It is well known that there is a degree of fuzzy uncertainty in land cover classification using remote sensing (RS) images. In this article, we propose a novel fuzzy uncertainty modeling algorithm for representing the features of land cover patterns, and present an adaptive interval type-2 fuzzy clustering method. The proposed fuzzy uncertainty modeling method is performed in two main phases. First, the segmentation units of the input multi-spectral RS image data are subjected to objectbased interval-valued symbolic modeling. As a result, features for each land cover type are represented in the form of an intervalvalued symbolic vector, which describes the intra-class uncertainty better than the source data and improves the separability between different classes. Second, interval type-2 fuzzy sets are generated for each cluster based on the distance metric of the interval-valued vectors. This step characterizes the inter-class high-order fuzzy uncertainty and improves the classification accuracy. To demonstrate the advantages and effectiveness of the proposed approach, extensive experiments are conducted on two multispectral RS image datasets from regions with complex land cover characteristics, and the results are compared with those given by well-known fuzzy and conventional clustering algorithms. | Hui HE Haihua XING Dan HU Xianchuan YU | 2019 | Science China Earth Sciences2019，62，2： | 2 |
| 5 | Unified control and detection framework and its applications: a review, some new results, and future perspectives显示文摘Initiated three decades ago,integrated design of controllers and fault detectors has continuously attracted research attention.The recent development of the unified control and detection framework with an observer-based residual generator in its core gives a more general form of the previous works.Its applications to residual centred modelling of uncertain control systems,fault detection in feedback control systems with uncertainties,fault-tolerant control(FTC)as well as control performance degradation monitoring,detection and recovery are introduced.In conclusion,some future perspectives are proposed. | DING Steven Xianchuan LI Linlin JIANG Bin | 2021 | Journal of Systems Engineering and Electronics2021，32，5： | 1 |
| 6 | Fabrication and electrochemical treatment application of a microstructured TiO 2 -NTs/Sb–SnO 2 /PbO 2 anode in the degradation of C.I. Reactive Blue 194 (RB 194)显示文摘 | Hao An Hao Cui Wenyi Zhang Jianping Zhai Yan Qian Xianchuan Xie Qin Li | 2012 | Chemical Engineering Journal2012，，： | 1 |
| 7 | A Hierarchical Method for Locating the Interferometric Fringes of Celestial Sources in the Visibility Data显示文摘In source detection in the Tianlai project,locating the interferometric fringe in visibility data accurately will influence downstream tasks drastically,such as physical parameter estimation and weak source exploration.Considering that traditional locating methods are time-consuming and supervised methods require a great quantity of expensive labeled data,in this paper,we first investigate characteristics of interferometric fringes in the simulation and real scenario separately,and integrate an almost parameter-free unsupervised clustering method and seeding filling or eraser algorithm to propose a hierarchical plug and play method to improve location accuracy.Then,we apply our method to locate single and multiple sources’interferometric fringes in simulation data.Next,we apply our method to real data taken from the Tianlai radio telescope array.Finally,we compare with unsupervised methods that are state of the art.These results show that our method has robustness in different scenarios and can improve location measurement accuracy effectively. | Rong Ma Ruiqing Yan Hanshuai Cui Xiaochun Cheng Jixia Li Fengquan Wu Zongyao Yin Hao Wang Wenyi Zeng Xianchuan Yu | 2024 | Research in Astronomy and Astrophysics2024，24，3： | 0 |
| 8 | Detection of Geochemical Element Assemblage Anomalies Using a Local Correlation Approach显示文摘As direct prospecting data,geochemical data play an important role in modelling prospect potential.Geochemical element assemblage anomalies are usually reflected by the correlation between elements.Correlation coefficients are computed from the values of two elements,which reflect only the correlation at a global level.Thus,the spatial details of the correlation structure are ignored.In fact,an element combination anomaly often exists in geological backgrounds,such as on a fault zone or within a lithological unit.This anomaly may cause some combination of anomalies that are submerged inside the overall area and thus cannot be effectively extracted.To address this problem,we propose a local correlation coefficient based on spatial neighbourhoods to reflect the global distribution of elements.In this method,the sampling area is first divided into a set of uniform grid cells.A moving window with a size of 3×3 is defined with an integer of 3 to represent the sampling unit.The local correlation in each unit is expressed by the Pearson correlation coefficient.The whole area is scanned by the moving window,which produces a correlation coefficient matrix,and the result is portrayed with a thermal diagram.The local correlation approach was tested on two selected geochemical soil survey sites in Xiao Mountain,Henan Province.The results show that the areas of high correlation are mainly distributed in the fault zone or the known mineral spots.Therefore,the local correlation method is effective in extracting geochemical element combination anomalies. | Xianchuan Yu Shicheng Wang Hao Wang Yuchen Liang Siying Chen Kang Wu Zhaoying Yang Chongyang Li Yunzhen Chang Ying Zhan Wang Yao Dan Hu | 2021 | Journal of Earth Science2021，32，2： | 0 |
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