## 3 Methodology of Processing
### Acquisition of Building Area  
The building area during the same period was acquired using the classification results of Landsat 8. The detailed information of workflow was divided into three parts. Firstly, the Landsat 8 OLI image that was achieved was pre-processed. Secondly, the land cover information was extracted by the object coverage classification method, which was verified with the traditional supervised classification and ground observations. Finally, the southern architectural coverage map along the Erhai region was obtained. Figure 2: Schematic Figure of Small Baseline Subset (SBAS) Configuration of Sentinel-1 Images. (**a,b**) Results of the descending flight direction; (**c,d**) the results of the ascending flight direction. Images are denoted by green or yellow solid circles (the yellow one represents the super master image) with a label of date in format: DD-MM-YY; all the lines connecting two images represent radar interferograms with a perpendicular baseline shorter than 500 m, where the blue line indicates those interferograms. The original image was radiated and calibrated with the radiation calibration parameters of the OLI sensor, and the Digital Number (DN) value of the original image was transformed into the radiance by pixel scale. Then, the image was corrected using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) model, and the radiance value was converted into the true reflectance of the surface [46; 47]. In addition, geometric precision correction was performed on the image, making the correction error controlled within 0.1 pixel. Finally, the Gram-Schmidt algorithm was used to fuse band 2, band 3, and band 4 of the corrected image, and the resolution of the fused image was 30 meters, which was helpful to the extraction of urban building classification information. Supervised classification was based on prior knowledge and trained by selecting samples in order to establish statistical recognition function and classify categories according to probability rules. The application of this method of classifying land use could guarantee the reliability and representativeness of the results. In this study, ENVI 5.3 software was used as the operating platform, and the maximum likelihood algorithm was used to supervise and classify remote sensing images in the Erhai region. This research distinguished elements visually on the image and drew the polygon samples, and tried to ensure the purity of the elements in the sample. It also drew other samples of the feature in other areas, and the sample was distributed as evenly as possible across the image. According to the research needs, the selected land-use samples were building land, water, agriculture, forest land, and bare land. Figure 3: Technical Flow Chart of the SBAS–InSAR (Interferometric Synthetic Aperture Radar) Technology. The training samples were selected according to the characteristics of the land-covering classification scheme and the sample description. The sample selection followed the principle of uniform distribution of the entire study area, and the region of interest was established through the visual interpretation in the Landsat 8 OLI image, combined with Google Earth high image optimization. In order to check the separability between each sample, the Jeffries-Matusita, Transformed Divergence parameters were used to investigate. When the values of those two parameters were between 0 and 2.0, greater than 1.9 indicated that the sample was separable and belonged to the qualified sample; less than 1.8 indicate a need to edit the sample or re-select the sample; less than 1 considered combining the two types of samples into a sample. The sample classification results were tested, and separation values of different types were greater than 1.9, which meant that the samples taken can be well differentiated. According to the classification of representative training samples, the maximum likelihood classification method was adopted in the southern Erhai region to obtain the building area.