基于多光谱遥感影像分类的城镇用地变化研究  

Urban Land Use Change Detection Based on High Accuracy Classification of Multispectral Remote Sensing Imagery

作　　者：童小华[1] 张学[1] 刘妙龙[1] 

机构地区：[1]同济大学测量与国土信息工程系,上海200092

出　　处：《光谱学与光谱分析》2009年第8期2131-2135,共5页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(40771174);教育部新世纪优秀人才支持计划基金项目(NCET-06-0381);上海市曙光计划和启明星跟踪项目(07SG24;08QH14022);高等学校博士学科点专项科研基金项目(20070247046)资助

摘　　要：选择上海城镇化较典型的边缘区域之一嘉定区为研究对象,基于4时相的多光谱遥感影像研究了该区域城镇用地变化情况。采用一种改进的遗传算法优化BP神经网络的遥感影像分类方法,进行了较高精度的土地覆盖类型分类。在此基础上,提取出研究区域的城镇用地类型,然后利用不同年份的4幅城镇用地图像中像素值与叠加得到的1幅城镇用地变化图像中像素值之间的16种对应关系,按板块统计研究区域内任意2个年份间隔内城镇用地变化的数量,从而检测了该区域在研究时间段内的城镇用地变化情况。In the present paper, the urban land change in Jiading district of Shanghai was studied on the basis of high accuracy classification for 4 epochs of multispectral remotely sensed imageries. A further improved genetic--algorithm optimized back propagation neural network approach was first employed in our study to obtain sorts of land cover types from the remotely sensed imageries. The urban land and non--urban land types were thus extracted based on the classification result. According to the 16 corresponding relationships between the pixel values in the four urban land imageries and the ones in the generated urban land change imagery, the amount of each type pixel in the generated imagery was calculated according to the four plates, and the situation of urban land change was analyzed and investigated for the study area in three year intervals. The urban development in the study area was also preliminarily revealed.

关 键 词：遗传算法 神经网络分类 遥感 城镇用地 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]