Urbanization has attracted wide and active interests due to the impact on regional sustainable development. As an important indicator of urbanization, impervious surface area (ISA) should be accurately monitored. In scenario of identifying ISA by supervised classification from satellite images, the training samples are usually labeled manually, which is highly labor-intensive and time-consuming. High-resolution nighttime light image provides a unique footprint of human activities and settlements which are strongly correlated with ISA. In view of this, a novel ISA training sample selection method is proposed by integrating the JL1-3B high-resolution nighttime light imagery and Sentinel-2 time series imagery, and the random forest is applied to classify ISA from Sentinel-2 imagery. The quality of the automatically selected samples was quantitatively validated. There were over three study areas, and the overall classification accuracies were above 97%, showing reliable and robust performance. Compared with conventional methods, the proposed approach achieves satisfactory results in separating bare land from ISA. This study provides a data fusion way which can automatically generate sufficient and high-quality training samples for ISA mapping, and suggests that high-resolution nighttime imagery could demonstrate a promising potential for urban remote sensing.

中文翻译：

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一种使用 JL1-3B 夜间光和 Sentinel-2 图像的不透水表面积映射的新样本选择方法

城镇化对区域可持续发展的影响引起了广泛而积极的关注。作为城市化的重要指标，不透水表面积（ISA）应得到准确监测。在从卫星图像中通过监督分类识别ISA的场景中，训练样本通常是手动标记的，这是高度劳动密集型和耗时的。高分辨率夜间灯光图像提供了与 ISA 密切相关的人类活动和定居点的独特足迹。鉴于此，结合JL1-3B高分辨率夜间灯光图像和Sentinel-2时间序列图像，提出了一种新颖的ISA训练样本选择方法，并应用随机森林对Sentinel-2图像中的ISA进行分类。对自动选择的样品的质量进行了定量验证。研究领域超过3个，总体分类准确率在97%以上，表现可靠稳健。与传统方法相比，所提出的方法在将裸地与 ISA 分离方面取得了令人满意的结果。本研究提供了一种数据融合方式，可以自动生成足够高质量的 ISA 制图训练样本，并表明高分辨率夜间图像可以展示城市遥感的广阔潜力。