针对监督分类提取不透水面需要人工获取大量训练样本的制约，提出了一种亚米级高空间分辨率夜光遥感影像引导下的不透水面自动提取方法。以夜光强度信息作为先验知识，判别对应地理位置的Landsat8 OLI影像像元为不透水面正负训练样本后，提取OLI影像的光谱和纹理特征构建特征集，利用集成ELM分类器提取不透水面。选择全球4个具有代表性的城市作为试验区进行验证，结果显示，该方法在4个试验区的不透水面提取精度均超过93%，Kappa系数均在0.87以上。对比BCI指数与人工选取训练样本的不透水面提取结果，发现该方法在4个试验区的总体精度均优于指数法，主要原因是该方法相较于BCI指数法可以更有效地区分裸土和不透水面。提出的自动提取方法在3个试验区的总体精度高于或接近人工样本分类方法，但在哈尔滨试验区的总体精度略低，主要是因为在自动选择样本过程中灯光强度弱的不透水面未被选为正样本导致部分漏提。研究表明，高分辨率夜光数据可以作用遥感影像解译与地物提取的先验知识，引导自动分类提取模型的构建，具有较高的实用性。

Supervised classification is a vital approach to extract impervious surface areas (ISA) from satellite images, but the training samples need to be provided through heavy manual work. To address it, this study proposed an automatic method to generate training samples from high-resolution night light data, considering that nighttime lights generated by human activities is strongly correlated with impervious surface. First, positive and negative samples for ISA were located according to the distribution of nighttime lights. Second, the feature sets were constructed by calculating the spectral and texture feature from the OLI images. Third, an ensemble ELM classifier was selected for ISA classification and extraction. Four large cities were selected as study areas to examine the performance of the proposed method in different environment. The results show that the proposed method can automatically and accurately acquire ISA with an overall accuracy higher than 93% and Kappa coefficient higher than 0.87. Furthermore, comparative experiments by biophysical composition index (BCI)and classification by manual sample were conducted to evaluate its superiority. The results show that our method has better separability for ISA and soil than the BCI. In general, the proposed method is superior to manual methods, except Harbin mostly because some impervious surfaces with weak light intensity are selected as negative samples.