近日，学院地理学院赵耀龙教授团队在地学与遥感领域主要期刊European Journal of Remote Sensing发表题为“Improving LSMA for impervious surface estimation in an urban area”的研究论文（全文链接：https://www.tandfonline.com/doi/full/10.1080/22797254.2021.2018666  ），本研究对现有估测不透水面的线性模型进行优化，可以为城市环境的遥感监测与应用提供更可靠的基础数据。澳门十大正规老牌网赌赵耀龙教授为本文通讯作者，学院北斗研究院特聘研究员王琎为本文第一作者。

对于在亚像元尺度下进行不透水面的估测，线性光谱分析（LSMA）最常用的方法之一。然而，线性模型难以精确反映像元间光谱反射率的微小差异，使遥感应用中本已存在的“异物同谱“更为严重。本研究利用高分辨率图像和实地调查数据，引入光谱角分析方法(SAM)，使线性模型的误差显著降低。与传统方法相比，本研究所提出的方法总体精度显著提高了10%以上，达到85.24%。此外，该方法可以对不透水地表和高密度不透水面进行高精度估测，为城市环境的管理与维持提供更为坚实的科学依据。

European Journal of Remote Sensing重点关注遥感技术的应用，是地学相关领域颇具影响力的期刊，近五年影响因子为3.8。

Linear spectral mixture analysis (LSMA) and regression analysis are the two most conventionally used methods to estimate impervious surfaces at the subpixel scale in an urban area. However, LSMA lacks the sensitivity to pixel brightness, which leads to inter variability of endmembers and affects the ability to distinguish features with a similar spectral signature. This research aims to develop LSMA aided by a regression analysis model to estimate impervious surfaces with higher accuracy. A spectral angle mapping (SAM) based regression analysis model is introduced to reduce errors. Based on high-resolution images and field survey data, the SAM-based regression analysis can estimate non-impervious surface and high-impervious surface densities with high accuracy, while less accurate in impervious surfaces with low/medium density. In contrast, LSMA is able to estimate low/medium-density impervious surfaces with higher accuracy. We propose an improved approach by integrating the two methods, regression analysis aided LSMA, for impervious surface estimation. The proposed method increases the overall accuracy of the impervious surface estimation to 85.24%, which is significantly greater than that of the conventional methods.

文字 | 王  琎

初审 | 袁亚娟

复审 | 陶  伟

终审 | 刘云刚