Training support vector machines (SVMs) for pixel-based feature extraction purposes from aerial images requires selecting representative pixels (instances) as a training dataset. In this research, locality-sensitive hashing (LSH) is adopted for developing a new instance selection method which is referred to as $DR.LSH$. The intuition of $DR.LSH$ rests on rapidly finding similar and redundant training samples and excluding them from the original dataset. The simple idea of this method alongside its linear computational complexity make it expeditious in coping with massive training data (millions of pixels). $DR.LSH$ is benchmarked against two recently proposed methods on a dataset for building extraction with 23,750,000 samples obtained from the fusion of aerial images and point clouds. The results reveal that $DR.LSH$ outperforms them in terms of both preservation rate and maintaining the generalization ability (classification loss).

用于从航空影像中基于像素的特征提取的训练支持向量机（SVM）需要选择代表性的像素（实例）作为训练数据集。在这项研究中，采用局部敏感哈希（LSH）来开发一种新的实例选择方法，该方法称为$d\mathrm{[R}。\mathrm{大号}\mathrm{小号}H$。的直觉$d\mathrm{[R}。\mathrm{大号}\mathrm{小号}H$依靠迅速找到相似且多余的训练样本并将其从原始数据集中排除。这种方法的简单思想及其线性计算复杂性使其迅速处理大量训练数据（数百万像素）。$d\mathrm{[R}。\mathrm{大号}\mathrm{小号}H$在针对数据集进行建筑物提取的两种最新提议的方法中，使用基准进行了基准测试，该方法使用了从航空影像和点云的融合获得的23,750,000个样本。结果表明$d\mathrm{[R}。\mathrm{大号}\mathrm{小号}H$ 在保存率和保持泛化能力（分类损失）方面都优于它们。