建筑物是一种极其重要的人工地物目标,通过提取建筑物可为城市规划、人口估算和景观分析等提供技术支撑。面向对象分类方法作为目前提取地物的有效手段之一,已广泛应用在建筑物信息的提取中。面向对象的形态学建筑物指数法具有较好的实用性,但提取稀疏建筑物的效果仍有待提高。为了解决这一问题,将中值绝对偏差应用于面向对象的建筑物提取,分别对建筑物密集和稀疏两种情况进行分析,采用查准率、查全率和F₁分数等指标对提取结果进行评价。实验结果表明,面向对象的中值绝对偏差法提取稀疏建筑物的效果明显优于面向对象分类方法和面向对象的形态学建筑物指数法。

Buildings are extremely important artificial feature objects. Extracting buildings can provide technical support for urban planning, population estimation, and landscape analysis. Object-oriented classification is an effective method for extracting ground objects and has been widely used in the extraction of building information. The object-oriented morphological building index method has good practicability, but the effect of extracting sparse buildings still needs to be improved. To solve this problem, the median absolute deviation is applied to the object-oriented building extraction, and the two situations of dense and sparse buildings are analyzed. Precision, recall, and F₁ score are used to evaluate the extraction results. Experimental results show that the object-oriented median absolute deviation method extracts sparse buildings significantly better than the object-oriented classification and object-oriented morphological building index methods.