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Extraction of built-up areas in Chinese silk road economic belt based on DMSP-OLS data

机译：基于DMSP-OLS数据的中国丝绸之路经济带建成区提取。

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摘要

Monitoring urban spatial information is vital to reveal the relationship between the human activity and environment, especially in the Chinese Silk Road Economic Belt, so as to allocate resources reasonably and realize sustainable development. To promote the remote sensing application in this field, a new method was proposed for urban built-up areas extraction mainly based on the support vector machine (SVM) classification with iterative sample refinement, combining Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data, and other auxiliary data such as Landsat images and the GlobeLand30 land cover product. Experiments were conducted by using the proposed approach for several cities in the southwest of the Chinese Silk Road Economic Belt, as classified by statistics and Landsat images. Compared with the traditional threshold dichotomy method and the state-of-the-art improved neighborhood focal statistics (NFS) method, the proposed method achieved better performance with respect to less relative error, and higher overall accuracy and Kappa coefficient.
机译：监测城市空间信息对于揭示人类活动与环境之间的关系至关重要，尤其是在中国丝绸之路经济带，以合理分配资源并实现可持续发展。为了促进遥感技术在这一领域的应用，提出了一种新的城市建成区提取方法，该方法主要基于支持向量机（SVM）分类和迭代样本细化，结合国防气象卫星程序-操作线扫描系统（DMSP- OLS）夜间灯光数据，以及其他辅助数据，例如Landsat图像和GlobeLand30地面覆盖产品。使用建议的方法对中国丝绸之路经济带西南部的几个城市进行了实验，并根据统计数据和Landsat影像进行了分类。与传统的阈值二分法和最新的改进的邻域焦点统计（NFS）方法相比，该方法在较小的相对误差，更高的整体精度和Kappa系数方面取得了更好的性能。

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