## 3 Results
### Extraction of Impervious Surface Distribution by Different Methods  
The five methods used for comparison are applicable to different conditions and provide higher accuracy in other cases [7; 8; 9; 10; 11]. They exhibited differences in performance after water bodies were masked in our three cases, and the average F1 scores of extractions by six methods from the lowest to the highest value were in the following order: the NDBI, NDISI, IBI, NDII, CBI, and our proposed method.  
The average F1 score of extractions by the NDBI after water bodies were masked was 32%. This index utilises the feature that the values for impervious surfaces in the SWIR1 band in Landsat 5 TM imagery are higher than those in the NIR band. However, the spectral features of impervious surfaces in Landsat 8 OLI imagery are different from those in Landsat 5 TM imagery because of the  
Figure 9: Ability of the proposed method to distinguish impervious surfaces from vegetation and bare soil: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3.  
differences in the sensor, wavelength range, and quantisation bit. Figure 2 shows that the values for impervious surfaces in the SWIR1 band were comparable to those in the NIR band in Cases 1 and 3, whereas the values in the SWIR1 band were lower than those in the NIR band in Case 2. Conversely, the bare soil in Case 3 gave higher values in the SWIR1 band than in the NIR band, which resulted in more instances of error.  
The average F1 score of extractions by the NDISI after water bodies were masked was 44%. The author who devised the NDISI pointed out that the values for impervious surfaces are higher than those of other ground objects in the thermal infrared band. This index led to instances of error in Cases 2 and 3, which were caused by the fact that the values for impervious surfaces in the Landsat 8 thermal infrared band were not always higher than those for other ground objects (Figure 2). Thermal radiation from impervious surfaces is more obvious in summer but may be lower than that from other ground objects in winter [39; 40]. Therefore, the use of the NDISI for the extraction of impervious surface distribution in summer may provide high accuracy [9], but its accuracy may be low if it is employed in winter.  
The average F1 score of extractions by the IBI after water bodies were masked was 47%. This index combines three indices, namely, the NDBI, MNDWI, and SAVI. In our cases, the NDBI values for impervious surfaces were not always higher than those for other ground objects such as the bare soil in Case 3 (Figure 10), which may have led to many instances of error.  
The average F1 score of extractions by the NDII after water bodies were masked was 57%. [PERSON] et al. pointed out that this index is unreliable because the values for impervious surfaces in the thermal infrared band are significantly lower than those in the visible light band [36]. Besides, this index requires that bare soil is masked in advance [10], which is difficult in practice.  
The average F1 score of extractions by the CBI after water bodies were masked was 65%. This index combines the PC1, NDWI, and SAVI, which represent high-albedo surfaces, low-albedo surfaces, and vegetation, respectively, but the ability to mask bare soil needs to be further enhanced [36].  
The average F1 score of extractions by our proposed method was 93%. Our method not only identified impervious surfaces but also masked other ground objects such as bare soil and therefore provided higher accuracy than the other five methods.