# Improving Accuracy of Impervious Surface Extraction Based on a Threshold Hierarchical Method (THM)
## 1 Introduction  
Impervious surface area (ISA) is an area with anthropogenic features (e.g., roads, driveways, sidewalks, parking lots, and rooftops) through which water cannot infiltrate into the soil [1]. As ISA is made up of various constructional materials, no individual ISA is a good representative of all of them [2]. It has been regarded as an important representation of urban area and a key environmental indicator [3]. Various remote sensing images (e.g., high- [4, 5], medium- [6, 7, 8] and coarse-resolution images [9, 10]) have been exploited to map ISA through various methods from regional, national, and global levels, considering the dynamic changes [9, 11, 12, 13] and local composition [4, 14, 15] of various aspects, such as hydrology [16, 17], urban heat island [18, 19], population estimation [20], water quality [21, 22, 23], rainfall runoff [16, 17], and climate change [18, 24] over the last three decades. The vegetation-impervious surface-soil (V-I-S) model, a conceptual model proposed by [PERSON] [25] for simplifying heterogeneous urban areas to a combination of three basic ground components, provided a theoretical basis for quantitatively understanding urban environments, such as the application of the spectral mixture analysis (SMA). The SMA, especially the liner spectral mixing analysis (LSMA), has been used to identify ISA and has proved to be an effective method to describe urban environment [1, 7, 12, 14]. It has been treated as one of the primary subpixel analysis approaches to estimate the percentage of ISA by analyzing three/four pure endmembers [26, 27]. The SMA based methods have a common problem [6, 27, 28, 27]: impervious surface is often overestimated in areas with a small amount of impervious surface (e.g., in the less-developed areas), but is underestimated in areas with a large amount of impervious surface (e.g., in thewell-developed areas). Previous studies indicate that bright colored building roofs are confused for dry soil, and dark-colored roads and buildings for water/wetland and shadows [14; 29]. Although many methods have been presented to solve the problem [27; 30], a reasonable solution is that the fully constrained linear mixture model requires that endmember fractions be positive and sum up to 1 [7]. The object-oriented method (OOM), rather than one run at a sub-pixel level, helps extract impervious surface at a pixel level. OOM is always undertaken considering high-resolution images where the resolution is under 10 m, and it exploits comprehensive information on the texture, context, and object-based features [31; 32]. Some studies found that SPOT data are better than that of Landsat TM at the sub-city level [33]. With an improvement in resolution, the proportion of mixed pixels was reduced significantly. For a particular zone, the larger the area of impervious surface in medium-resolution imagery (such as Landsat), the more likely it has purely impervious surface in the high-resolution imagery, which is better than that of Landsat 8 OLI. The OOM could be employed for extracting impervious surface in areas with high amounts of impervious surface. Thus, for a zone with a larger amount of impervious surface, if the OOM is applied with high-resolution, it could help mitigate the underestimation of impervious surfaces in areas with a large number of impervious surfaces brought out by the linear spectral mixture model, helping achieve higher extraction accuracy. Here, a threshold hierarchical method (THM) was designed to address the underestimation of the number of impervious surfaces in areas with a large amount of such surface brought out by an LSMA. Thresholding is the simplest and most fundamental method for image segmentation. The range of the area with a larger amount of impervious surface is extracted considering a threshold value of the number of impervious surfaces, which is calculated through LSMA.