The high spatial resolution Remote Sensing image has richer information than the low or middle resolution image, such
as structure and texture information. Traditional image classification technology which only uses spectral information of
pixels is not suitable for the high resolution image. In order to make full use of the rich information, object-oriented
thought is introduced into the high resolution information extraction. In contrast to traditional methods, the basic
processing units of object oriented image analysis are image objects, and not single pixels. It could fully integrate
spectral values and spatial information such as: shape, size and contextual relationship. The objective of this study is to
extract kinds of information from QuickBird image of the urban area using the object-oriented information extraction
approaches. Image processing includes geometric correction, HIS fusion, image segmentation and classification using
the integration of fuzzy classification and the nearest neighbor (NN). 84.82% overall accuracy is achieved with this
approach, while only 73.87% is achieved with traditional pixel-based method. It shows that object-oriented approach is
promising in providing detailed and accurate information about the physical structure of urban areas from the high
With the development of technology of remote sensing and computer, the quantity of data and information is increased greatly. After a long-time research, we find that it is impracticable to manage and handle information only using information. Only decision-making system can do it, Decision Tree is an important method in classification of land use and land cover. This paper gives the formation process from building synthetic database to designing decision tree model, knowledge base to provide some forms of data for extracting LUCC information, there are three aspect data include about: 1) grid data such as remote sensing data TM, SPOT, 2) Ground-measured data such as DEM, and 3) thematic Vector data such as land use data and so on. Regularity base consists of all the transformation rules from the source state to the destination of the problem, each dot include at least one rule, the foundation can resolve recognize where change in land area and type. At last, according to the level of complexity of the change of LUCC, it gives two kinds of decision tree models: the classified comparative between single result and the synchronic analysis with multi-temporal images.
(1) The classified comparison between single results. We take the information extracting for ice changes as an example, and the result is very ideal. (2) The synchronic analysis with multi-temporal images. We construct decision tree in Hei bei, the condition include the grey value and the other features such as slope gradient and GIS thematic supported data, the result shows that the biggest change type is that other lands are transferred to the forest. The area precision is excess to 85%, and the sort precision 90%.