Abstract: Urbanization has been an important social and economic phenomenon taking place in an unprecedented scale and rate all over the world. But the uncontrollable urbanization has negative impacts on the society, ecology, and economy. Therefore, it is of great importance to detect urban area in order to prevent these negative consequences. In this paper, the method detecting Built-up Areas（BAs） based on single-polarized TerraSAR-X images is improved and perfected, and combines ancillary optical satellite images to identify urban area of Changzhou City. The approach starts from the existing method that involves analysis of the local speckle characteristics and intensity information to monitor spatial extent of the built-up area and non-built-up area. Then the maximum likelihood method and post-classification are used to extract the demolition area within cities based on the Landsat8 image. The final extraction of urban area includes the above built-up area and demolitionarea. In the end of this study, it uses remote sensing images from Google Earth to generate confusion matrix to evaluate the accuracy of extraction of urban area. The results show that the overall classification accuracy is about 89%, indicating that this method is feasible.

Key words: TerraSAR-X, regional growing, Landsat, urban area, urban sprawling

摘要： 城市扩张作为一个重要的社会和经济现象，正以前所未有的速度和规模在世界各地不断推进。但是盲目的城镇扩张会对社会、生态及经济等产生一定的影响。因此准确高效地提取城镇用地，为城镇规划等提供决策依据变得尤为重要。以常州市为例，对基于单极化TerraSAR-X影像提取建成区方法进行了改进和完善，结合光学卫星影像辅助提取城镇范围，以提高这一类城市城镇提取的精度。该方法首先通过分析局部散斑特性和强度信息，采用基于阈值的方法提取城市和非城市区域，然后再利用Landsat8图像，采用最大似然法和分类后处理来提取城镇范围内的拆迁区域，最后结合提取的城市区域和拆迁区域来完成城镇用地的提取。最后，该研究中，利用Google Earth的遥感影像图进行交叉验证生成混淆矩阵来评价城镇用地的提取精度，其总体分类精度约为89%，表明该方法行之有效。

关键词: TerraSAR-X, 区域生长, Landsat, 城镇用地, 城市扩张