## 2 Materials and Methods
### Image Segmentation and Classification  
In a digital image processing application, the primary process is segmentation to detect and identify objects and components within the image. The segmentation process divides the image into parts known as constituent objects. Automatic segmentation is generally the most challenging image processing [12]. With the development of image processing algorithms, image segmentation is also developed using region growing and merging, namely by expanding pixels so that the object becomes larger. In the end, some objects close to the same value will merge into one other, bigger object. This mathematical  
Figure 3: Radar wave backscatter mechanism on the surface of the object (**a**) Grass/Land, (**b**) Vegetation, and (**c**) Urban in normal and flood conditions, for short wavelengths (C- and X-band) and long waves (L- and P-band). algorithm is the basis for developing an image segmentation algorithm that carries out the unsupervised segmentation process without human intervention. [PERSON] et al. created a SAR satellite data processing algorithm to detect urban floods near-real-time using data before and after a flood event. Furthermore, the image is classified using a supervised classification to obtain the flood area based on building classes. The developed Probability Density Function (PDF) method can reduce the maximum backscatter intensity difference for rice fields and open areas by 35 dB; however, for urban areas, it has increased by 25 dB [9]. Further development of this method can reduce variance by 12 dB and increase urban areas by 15 dB [10]. In comparison, [PERSON] et al. [46] used PDF to estimate the maximum similarity before thresholding by comparing the Otsu, Split-KI (Kettler and Illingworth), and Local Thresholding (LT) methods. The Overall Accuracy (OA) results obtained from the Sentinel-1a image classification in the Louisiana plain were 98.12% (Otsu), 98.55% (Split-KI), and 98.91% (LT), respectively. Figure 4: Sample images from co-polarization (VV) and cross-polarization (VH): (**a,b**) Pre-flood event; (**c,d**) Co-event during January 2020 flood; (**e,f**) Post-flood event. [PERSON] et al. proposed the creation of a large-scale global database for flood inundation maps derived from the SAR dataset [28]. The method used is histogram thresholding to delineate quickly, then the level of flood distribution is extracted from the SAR backscatter using the Probability Density Function (PDF). Thresholding is performed using the Hierarchical Split-Based Approach (HSBA) to identify pixels with a bimodal distribution on the sub-pixels, which indicates that there is an immersion limit on these pixels [47]. The accuracy of the results obtained from flood detection in rural areas is 35%. Figure 5: Sentinel-1a images: (**a**) co-polarized (VV); (**b**) cross-polarized (VH); (**c**) flood markers. Another technique in flood detection is to utilize the polarization characteristics of radar signals, namely the Interferometric SAR (InSAR) method. The principle of stable scatterer or persistent scatterer is used to detect areas that do not experience changes in reflection characteristics, while changes in reflection characteristics result in low coherence between image data and are assumed to be flooded. The mapping is built by creating 20 interferometric pairs from 22 consecutive Sentinel-1a images with a composition of 17 pairs of pre-event images, a pair of images during a flood, and two pairs of post-event images [48]. [PERSON] et al. also integrated intensity data using InSAR coherence, normalized cross-correlation to detect the presence of water in urban areas and mapping of double-bounce-producing objects using histogram thresholding and region growing. Pixels are categorized as floods when there is a decrease in coherence on the RGB composite channel [16]. In line with the development of the field of artificial intelligence, image processing methods also develop by making use of artificial intelligence functions.