Research On Urban Vegetation Information Extraction Based On Object-Orient Analysis And Green Quantity Estimation

Urban virescence is an important part of the city ecosystem. High-resolution satellite image is adopted to extract the urban virescence information (The types, distributing and structure of urban vegetation) immediately and exactly, which is an important basis for evaluating quality of city ecosystems, and meeting the demand of deparment for urban planning.In this paper, the IKONOS image is used to extract urban vegetation through object-orient classification method. How to select optimal segmentation scale is discussed. in urban vegetation extraction.Then, the urban vegetation was extracted by an established class hierarchy. To increasing the accuracy of urban "Green Quantity" estimated, The optimized BP Neural Network model is used to estimating urban "Green Quantity" based on Genetic Algorithms The main contents and results are as follows:1. To enhance image interpretation ability, IKONOS PAN is merged with multi-spectral image in the image pro-processing. By anlysing four methods of image fusion, the PCA transformation is the best method for vegetation classification. Because of the building shadow and Terrain effect in high-resolution image, the spectral of geographic entity distorted seriously. This paper adopts different method to rectify mountain shadow and building shadow. On one hand, the building shadow was automated extraction by object-orient method and corrected by the Lambertian model;on the other hand, the mountain terrien normalization was corrected by Statistic-empirical method using DEM data. The experiment shows that Lambertian model was overcorrection seriously;but Statistic-empirical method can get better performance.2. In this paper, urban vegetation information was extracted by object-orient Method. A new method is developed for selecting optimal segmentation scale. Urban vegetation is classificated by a class hierarchy based on spectral, texture and context information which are established though commercial software. The gross accuracy is 85.5% and Kappa coefficient is 0.826. The experiment shows that object-orient method lead to improve urban vegetation classification accuracy compared to pixel-based classification method.3. Remote sensing is the source of geting the urban "Green Quantity" information. Based on BP Neural Network model, which is established, this paper changed two estimation parameters. It adopt environment factor and elevation factor which are correlate with vegetation, discard texture factor which is little correlate with vegetation. Then optimize the BP Neural Network model by Genetic Algorithms. The experiments show that the estimation of city "Green Quantity" increased because of adopting environment factor and elevation factor. The Neural Network model training was more stable because optimized by Genetic Algorithms.