城郊高分影像中利用阴影的建筑物自动提取

提出了一种充分利用阴影实现自动分类与后处理相结合的建筑物自动提取方法:首先根据阴影和植被自动检测结果并选定裸地样本确定预分类CMap图，并设计了基于偏移阴影分析的建筑物样本自动提取方法，结合支持向量机（support vector machines，SVM）分类模型将影像分为阴影、植被、建筑物、裸地4大类以提取建筑物初始结果；通过形态学处理提升区域完整性，区域增长补充漏检区域，利用设计的相交边界阴影比率筛除无阴影的非建筑物等措施，进行后处理优化获取最终结果。实验表明，充分利用阴影信息，不仅能准确、全面地获取各类样本，保证分类精度，与后处理优化策略紧密结合，大幅度提高了正确率和完整度；并且自动化程度得到有效提高，更适用于城郊区域建筑物的提取。

Automatic Building Extraction Based on Shadow Analysis from High Resolution Images in Suburb Areas

This paper describes an automatic method for building extraction from high resolution images. It consists of automatic classification and automatic post-processing using shadow information. Shadow and vegetation are automatically detected; bare land samples are selected manually. In addition, by analyzing the distribution categories in the shifted shadow regions, building sample regions are acquired automatically. Based on these four categories of samples, the classification can be implemented using a SVM classifier, from which the initial building results are extracted. The results are optimized in post-processing, including mathematical morphology processing to enhance the completeness of detected regions, region growth to supplement undetected building regions, as well as non-building removal using shadow rates on the intersection boundaries. Optimization yields the final results. Experimental results indicate thatan approach using shadow analysis can extract building sample regions accurately, completely, and automatically, guaranteeing classification precision. The post-processing strategy effectively improves the completeness of building region detection and removes most non-buildings without shadows. Therefore, the accuracy of the final result increases greatly. In all, the automation of this method is high, and applies to buildings in suburb areas.