面向对象的高分辨率遥感影像城区建筑物分级提取方法  

Object-oriented Method of Hierarchical Urban Building Extraction from High-resolution Remote-Sensing Imagery

作　　者：陶超[1,2] 谭毅华[1,2] 蔡华杰[1,2] 杜博[1,2] 田金文[1,2] 

机构地区：[1]华中科技大学图像识别与人工智能研究所,湖北武汉430074 [2]华中科技大学多谱信息处理技术国家重点实验室,湖北武汉430074

出　　处：《测绘学报》2010年第1期39-45,共7页Acta Geodaetica et Cartographica Sinica

基　　金：国家863计划(2007AA12Z153);民用航天预研计划

摘　　要：提出一种高空间分辨率遥感影像城区建筑物自动提取方法。该方法将面向对象的思想融入到基于邻域总变分的建筑物分割方法中,并通过分析分割后不同类型建筑物提取的难易程度,提出一种多特征融合的建筑物对象分级提取策略:首先通过形状分析检测一部分分割完整的矩形建筑物目标,然后采用新提出的多方向形态学道路滤波算法将建筑物与邻近光谱相似的道路目标分离,确保每一个候选建筑物目标都是独立的对象,最后利用初提取的建筑物对象和已剔除的非建筑物对象作为样本建立概率模型,根据贝叶斯准则进行建筑物后提取。实验表明:该方法可以检测同一幅影像中具有不同形状结构和光谱特性的建筑物目标,准确率高、鲁棒性好。An automatic urban building extraction method for high-resolution remote-sensing imagery, which combines building segmentation based on neighbor total variations with object-oriented analysis, is presented in this paper. Aimed at different extraction complexity from various buildings in the segmented image, a hierarchical building extraction strategy with multi-feature fusion is adopted. Firstly, we extract some rectangle buildings which remain intact after segmentation through shape analysis. Secondly, in order to ensure each candidate building target to be independent, multidirectional morphological road-filtering algorithm is designed which can separate buildings from the neighboring roads with similar spectrum. Finally, we take the extracted buildings and the excluded non-buildings as samples to establish probability model respectively, and Bayesian discriminating classifier is used for making judgment of the other candidate building objects to get the ultimate extraction result. The experimental results have shown that the approach is able to detect buildings with different structure and spectral features in the same image. The results of performance evaluation also support the robustness and precision of the approach developed.

关 键 词：高分辨率遥感影像 建筑物提取 面向对象 形态学 贝叶斯准则 

分 类 号：P237[天文地球—摄影测量与遥感]