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Neural Network Based on Rough Sets and Its Application to remote sensing image classification

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Geo-Spatial Information Science 2002年第2期5卷 17-21页

作者： WUZhaocong LIDeren associateprofessor Ph.Dcandidate.SchoolofRemoteSensingInfomationEngineeringWuhanUniversity129LuoyuRoadWuhan430079China associateprofessor Ph.Dcandidate.SchoolofRemoteSensingInfomationEngineeringW

This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi_spectral image is *** successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.

关键词： rough sets back propagation neural network remote sensing image classification

A RBF classification method of remote sensing image based on genetic algorithm

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Journal of Harbin Institute of Technology(New Series) 2006年第6期13卷 711-714页

作者：万鲁河张思冲刘万宇臧淑英 College of Life and Environmental Science Harbin Normal University College of Forestry Northeast Forestry University Harbin 150040 China

The remote sensing image classification has stimulated considerable interest as an effective method for better retrieving information from the rapidly increasing large volume, complex and distributed satellite remote imaging data of large scale and cross-time, due to the increase of remote image quantities and image resolutions. In the paper, the genetic algorithms were employed to solve the weighting of the radial basis faction networks in order to improve the precision of remote sensing image classification. The remote sensing image classification was also introduced for the GIS spatial analysis and the spatial online analytical processing (OLAP), and the resulted effectiveness was demonstrated in the analysis of land utilization variation of Daqing city.

关键词： genetic algorithm radial basis function networks remote sensing image classification spatial online analytical processing GIS

How to accurately extract large-scale urban land?Establishment of an improved fully convolutional neural network model

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Frontiers of Earth Science 2022年第4期16卷 1061-1076页

作者： Boling YIN Dongjie GUAN Yuxiang ZHANG He XIAO Lidan CHENG Jiameng CAO Xiangyuan SU Chongqing Smart City Institute Chongqing Jiaotong UniversityChongqing 400074China Chongqing Geomatics and remote sensing Center Chongqing 401147China

Realizing accurate perception of urban boundary changes is conducive to the formulation of regional development planning and researches of urban sustainable *** this paper,an improved fully convolution neural network was provided for perceiving large-scale urban change,by modifying network structure and updating network strategy to extract richer feature information,and to meet the requirement of urban construction land extraction under the background of large-scale low-resolution *** paper takes the Yangtze River Economic Belt of China as an empirical object to verify the practicability of the network,the results show the extraction results of the improved fully convolutional neural network model reached a precision of kappa coefficient of 0.88,which is better than traditional fully convolutional neural networks,it performs well in the construction land extraction at the scale of small and medium-sized cities.

关键词： improved fully convolutional neural network remote sensing image classification city boundary precision evaluation

A new ensemble feature selection and its application to pattern classification

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控制理论与应用（英文版） 2009年第4期7卷 419-426页

作者： Dongbo ZHANG Yaonan WANG Institute of Information Engineering Xiangtan University Xiangtan Hunan 411105 China College of Electrical and Information Engineering Hunan University Changsha Hunan 410082 China

Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.

关键词： Rough sets reduction Ensemble feature selection Neural network ensemble remote sensing image classification