Study On The Methods Of Object Classification And Its Application On Urban And Rural Plan Monitoring With High Spatial Resolution Remotely Sensed Data

With the development of the remote sensing (RS) technology, the high spatial resolution satellite remote sensing data are presented and the aerial remote sensing technology has been full developed, which provided good data source for the RS monitoring of the urban and rural plan. So the WorldView-Ⅱ data and the high spatial resolution aerial RS image are used in this paper. To the need of the monitoring and management of urban and rural, the object features computing algorithm and feature selection algorithm and classification based object-oriented (OO) methods for urban and rural land use are studied and used in the urban and rural plan monitoring. The main contents and the results are as following:Based on the high spatial remote sensing image segmentation algorithm, the feature computeing algorithm are studied. The obvious texture and shape features of the city object are focused on The scale of texture computing are studied based on semivariogram and the object texture computing algorithm are studied basen grey-level co-occurrence matrix. To the characteristic of the city building objects, several shape features computeing are studied in this paper.There are many features of the object such as spectral features, texture features, shape features and so on. The classification effective will reduce and the classification precision may not be improved with the total features of the objects. To the feature selection of the object-oriented classification, the technology flow of feature selection is formed firstly. The enhanced ReliefF algorithm is adopted to filter the irrelevant features. Then the mutual information among features is computed to eliminate the redundant features. At last, the genetic algorithm is used and the distance between the same classes and the different classes acted as the fitness function.the features set are searched and evaluated, which is beneficial to the city land use classifcication.To the problem of the small sample size in the object-oriented classification, and the support vector machine (SVM) is fit to the small sample size problem, parts of application SVM in the OO classification are studied in this paper. The classification precision are severely impacted by the C and γ parameters in SVM based radial basis function. Based on the research of the former and combined with the need of the urban and rural plan monitoring and management, the three step search method are used to parameters optimization. The SVM is to the two classes, so the multiple classes flow is designed in this paper. After classification based on the vote, further the Nearest Neighhood (NN) classifier are used to the case of vote equiation and one difference vote.Two area in Beijing are the study area and the WorldView-Ⅱ data and the aerial RS image with0.2m spatial resolution are used as the main data sources. The classification system of the urban and rural land use with RS data are designed. Based on the image segmentation and the features computeing and features selection algorithm presented in this paper, then the urban and rural land use is classified with the improved SVM classification. At last comparing with the urban and rural plan map of the area, the urban and rural plan are monitored and evaluated.