土地利用变化检测既是全球变化研究的重要内容，又是资源可持续利用中进行科学决策和管理的重要依据。利用遥感技术进行土地利用变化检测是获取土地利用变化信息最为经济有效的方式。随着遥感技术的发展和卫星空间分辨率的提高，高分辨率遥感图像开始广泛应用于各个领域。由于高分辨率遥感图像具有细节信息丰富、地物几何结构明显、空间信息丰富等特点，这使高分辨率遥感图像的处理技术与方法在某种程度上发生根本性的变化,传统的遥感图像处理技术不再适用于高分辨率遥感影像,需要综合分析现有各种变化检测技术和方法 ,设计适宜高分辨率遥感影像变化检测的技术路线。 上海交通大学遥感科学实验室依托上海市科委重大项目“基于影像内容的自动搜索和特定目标的变化检测与更新技术研究”（NO. 055115018），设计实现了面向对象的遥感图像处理系统ELU。本文结合该项目而进行，对高分辨率遥感图像的变化检测做了深入的研究，并完成了ELU系统中变化检测模块的设计和实现。 本文在研究常用的变化检测方法后，研究了基于历史土地利用矢量数据的变化检测的方法，并探讨了一种适合高分辨率遥感影像的变化检测方法。具体研究内容如下： 首先，本文对矢量数据和遥感影像相结合的工作模式进行了研究。提出了基于栅格坐标的矢量和栅格相结合的工作模式，研究了一种利用矢量边界快速准确提取对应的栅格数据的方法和遥感图像分割图快速矢量化的方法。其次，本文对基于矢量的变化检测算法进行了研究。针对高分辨率遥感影像提出采用图斑中相邻变化像素的面积来判断图斑变化，它不仅要考虑像素的变化，还要考虑变化像素的空间关系，单个孤立的或几个相邻的变化像素不能确定图斑的变化。还综合各检测算法的优点提出多种检测算法组合的方法。最后，在检测完毕后，对矢量数据的更新进行了研究。通过对变化图斑进行分割、分类和矢量化生成新的矢量数据。

Change Detection of land use is not only important content of Global Change research, but also important basis of scientific decision-making and management in sustainable use of resources. Change Detection of land use by Remote Sensing is the most effective way to get information of land use change. High-resolution remote sensing images are applied in many fields with the fast-development of remote sensing technology and the improvement of satellite spatial sensors resolution. With more details, clearer geometry feature and more space information, the operation of high-resolution remote sensing image change fundamentally. Traditional remote sensing image processing will no longer apply to high-resolution remote sensing images. It needs comprehensive analysis of existing change detection technology and methods, and design Technical route of change detection suitable for high-resolution remote sensing image. An object-oriented remote sensing image processing system named ELU is designed and implemented by Remote Sensing Laboratory of Shanghai Jiao Tong University, which supports in part by the research project of Content-based Search in Image and Change Detection and Auto-updating of Special Target. It is administered by Shanghai Science and Technology Committee. Change detection of High-resolution remote sensing images is relatively deeply studied in this thesis supported by the project. Change detection module of ELU system is also designed and implemented according to the study. After study of the normal methods of changing detection, this paper mainly researches remote sensing changing detection method which is based on historical land use vector data, and explores change detection methods suitable for high-resolution remote sensing images. Specific studies as follows: First of all, this paper studies a work pattern combined vector data and raster data which based on raster coordinate system. researeh the way to use vector boundary to quickly extract the raster image data and a fast method of vectorization for remote sensing segmentation map. Secondly, this paper studies change detection methods based on vector data. Aiming to high-resolution remote sensing images, this paper uses the area of change pixel in adjacent to determine the span. Besides change of pixel, we must also consider change in the space between pixels, a single isolated or several adjacent change pixels can not determine the changes in span. Propose method Combined with many change detection algorithms integrated the advantages of the change detection algorithms. Finally, after the change detection, this paper studies updating of vector data through segmentation, classification and vectorization for the change span, product the new vector data.