Fine scale land cover classification of urban environments is important for a variety of applications. LiDAR data has been increasingly used, separately or in conjunction with other remote sensing data, for providing land cover classification due to its high geometric accuracy as well as its additional radiometric information. An important issue in the classification of remote sensing data is the inevitable imbalance of training samples, which usually results in poor classification performance in classes with few samples (minority classes). In this paper, a synergy of sampling techniques in data mining with ensemble classifiers is proposed to address the data imbalance problem in the training datasets. Several sampling strategies, including under-sampling the majority classes, synthetic over-sampling the minority classes, hybrid-sampling, and under-sampling aggregation are examined. The results from two different datasets show superior performance of ensemble classifiers when integrated with sampling techniques. In particular, under-sampling aggregation and hybrid sampling coupled with random forests resulted in 16.7% and 5.5% improvements in the G-mean measure in two experimental datasets examined.

城市环境的精细规模土地覆盖分类对于多种应用至关重要。LiDAR数据由于其较高的几何精度及其附加的辐射测量信息，已越来越多地单独或与其他遥感数据结合使用来提供土地覆盖分类。遥感数据分类中的一个重要问题是训练样本不可避免的失衡，这通常会导致在样本较少的类别（少数种族类别）中分类性能较差。为了解决训练数据集中的数据不平衡问题，本文提出了一种采用集成分类器的采样技术协同作用。有几种采样策略，包括对多数类别进行欠采样，对少数类别进行综合过采样，混合采样，以及欠采样汇总。来自两个不同数据集的结果表明，与采样技术集成后，集成分类器具有出色的性能。尤其是，在两个实验数据集中，欠采样聚合和混合采样与随机森林相结合，导致G均值测量值分别提高了16.7％和5.5％。