• 论文 •

基于影象融合的干旱区城镇居民地信息提取研究

1. 1. 南京师范大学地理科学学院, 江苏 南京 210097;
2. 中国科学院 地球环境研究所黄土与第四纪地质国家重点实验室, 陕西 西安 710075
• 收稿日期:2005-05-10 修回日期:2005-09-20 出版日期:2006-07-20 发布日期:2006-07-20
• 基金资助:
欧盟资助项目"Sustainable Agroecosystem Management and Development of Rural-Urban Interaction in regions and cities of China(SUSDEV-CHINA)"(Contract number:ICA4-CT-2002-10004)、中国科学院知识创新工程项目(KZCX3-SW-146)资助。

A New Method in Retrieving Urban Residential Areas in Arid Region Based on Image Fusion of SAR and TM

WU Hong-An1, JIANG Jian-Jun1,2, ZHOU Jie2, ZHANG Hai-Long1, ZHANG Li1, XIE Xiu-Ping2

1. 1. The College of Geography Science, Nanjing Normal University, Nanjing, Jiangsu 210097;
2. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, Shaanxi 710075
• Received:2005-05-10 Revised:2005-09-20 Online:2006-07-20 Published:2006-07-20

Abstract: With the impletion of the policy of great development of Western China, the process of urbanization in the western China has increased rapidly, especially for some big cities. At the same time, urbanization also affected urban periphery ecological environment deeply. So how to get information about urban residential areas in time and accurately is important to urban monitoring. The study area is in the Weihe Plain, where the precipitation is low and barrens are distributed widely. In this research, to retrieve urban residential areas in the west of China, Radarsat SAR image and Landsat TM image were emerged using HIS transformation and then 2-class supervised classification was used to obtain urban land use information, that were urban areas and non-urban areas. This method takes advantage of both multi-spectral image and radar image. As we know, in the TM image the spectral features of urban residential areas and barrens are so similar that we could not distinguish them. However, the radar images can differentiate urban residential areas from barrens, for SAR data are sensitive to residential areas. To reveal the advantage of this method, the TM image was also classified into 5 land covers, namely urban residential areas, farmland, woodland, water body, and uncultivated land (including barrens) using traditional supervised classification. By comparing the two different methods, we find that the urban residential areas derived from the merged image of TM and SAR is more accurate than that using TM image only, the overall accuracy of them are 84.21% and 71.79%, respectively. So we considered that the image fusion method is an effective way to retrieve urban residential areas for the arid region where barrens are distributed, which could not only obtain all the residential information, but also eliminate barrens, thus the retrieving accuracy is very high.

• TP751.1