ABSTRACT

Many efforts have been devoted to extracting impervious surfaces based on different methods from multiple spatial resolution images. Differences in extraction methods and spatial resolutions are significant and have led to discrepant performances in terms of the impervious surface extraction accuracy. However, which extraction method is more suitable for which kind of spatial resolution image in practice is poorly understood. This study systematically compared the performances of 12 methods of impervious surface extraction for four spatial resolution images (i.e. Landsat 8 [30 m], Sentinel-2A [20 m], Sentinel-2A [10 m], and Gaofen-2 [4 m]) in three testing areas. The results indicated that for the medium-spatial resolutions of 30 and 20 m, the support vector machine (SVM) method was considered as the optimal classification method with the highest accuracy of impervious surface extraction. For the high-spatial resolutions of 10 and 4 m, the object based image analysis (OBIA) method obtained the highest accuracy of the impervious surface distribution. Furthermore, the perpendicular impervious surface index (PISI) outperformed the other indices in obtaining the impervious surface distribution, with the highest accuracy for four spatial resolution images. These comprehensive assessments can provide a valuable guidance for future impervious surface extraction from different spatial resolutions.

摘要

许多努力致力于基于不同方法从多个空间分辨率图像中提取不透水表面。提取方法和空间分辨率的差异是显着的，并导致在不透水表面提取精度方面的性能差异。然而，在实践中，对于哪种空间分辨率图像更适合哪种提取方法，人们知之甚少。本研究系统地比较了 12 种不透水表面提取方法对四幅空间分辨率图像（即 Landsat 8 [30 m]、Sentinel-2A [20 m]、Sentinel-2A [10 m] 和 Gaofen-2 [4 m]）的性能。 ]) 在三个测试领域。结果表明，对于 30 和 20 m 的中等空间分辨率，支持向量机（SVM）方法被认为是不透水面提取精度最高的最优分类方法。对于 10 和 4 m 的高空间分辨率，基于对象的图像分析 (OBIA) 方法获得了不透水表面分布的最高精度。此外，垂直不透水表面指数（PISI）在获得不透水表面分布方面优于其他指数，对于四个空间分辨率图像具有最高的精度。这些综合评估可以为未来从不同空间分辨率提取不透水表面提供有价值的指导。基于对象的图像分析（OBIA）方法获得了不透水表面分布的最高精度。此外，垂直不透水表面指数（PISI）在获得不透水表面分布方面优于其他指数，对于四个空间分辨率图像具有最高的精度。这些综合评估可以为未来从不同空间分辨率提取不透水表面提供有价值的指导。基于对象的图像分析（OBIA）方法获得了不透水表面分布的最高准确度。此外，垂直不透水表面指数（PISI）在获得不透水表面分布方面优于其他指数，对于四个空间分辨率图像具有最高的精度。这些综合评估可以为未来从不同空间分辨率提取不透水表面提供有价值的指导。