## 1 Introduction  
Natural disasters are common phenomena in all parts of the world. There are many types of natural disasters [1], of which a flood is considered to be one of the most destructive, widespread and frequent disasters [2; 3]. Every year, tremendous loss of life and property is caused by flooding [3]. Due to the changes in global climate and land use, floods are becoming more severe and more frequent all around the world [3; 4]. Although it is difficult to prevent floods, it is possible to minimise their impact through proper rescue, relief and resource allocation for recovery and reconstruction. Therefore, accurate inundation mapping, especially near real time, is very important for establishing a fast response plan and mitigating the disaster [5; 6; 7]. Traditional methods for inundation mapping are based on ground survey and aerial observation. However, when the flood spreads to a large scale, these approaches are time- and resource-consuming, which cannot satisfy the need for a fast response to a disaster. Moreover, aerial observation can be unrealistic in some extreme weather conditions, and the density of gauging stations is not satisfactory in many countries [8]. An alternative choice is provided by satellite remote sensing (RS) techniques [6]. Due to their time availability and cost effectiveness, satellite data has played an important role in understanding in simulation [9; 10; 11; 12; 13]. The availability of multi-date images makes it possible to monitor the progress of floods. Satellites used for mapping floods can be divided into those that are optical and those that are microwave. Due to its capacity to penetrate the frequent clouds in a flood event, microwave remote sensing is all-weather and invaluable for flood monitoring. With multispectral images, the flood can be analysed in a more straightforward way with simpler pre-processing [14]. In this study, we mainly focus on methods using multispectral satellite images. Numerous methods have been proposed for mapping inundation using multispectral remote sensing images. Among them, the one most frequently used is thresholding. Usually, indices are first calculated through different band combinations, such as the normalised difference water index (NDWI) created by [PERSON] [15], which has been proven to produce good results for inundated areas [16]. Then a threshold is selected to determine the water range in the image. A manual threshold is accurate, but has difficulty satisfying the need for fast disaster response. Moreover, it is subjective, as different operators may produce different results. To overcome the problems, unsupervised thresholding methods have been proposed. For instance, [PERSON] et al. [17] introduced [PERSON]'s algorithm to implement automatic selection of the water threshold. But due to the common illumination differences and mixed pixels in satellite images, its effectiveness is reduced, especially for some complicated scenes. The segmentation (semi-supervised) technique [18] has been proposed to minimise the involvement of the user. The user first selects some seed points, with which the connectivity map is generated using fuzzy logic. For example, in [19], a fast flood map and a detailed flood map were obtained using growing strategies with seed points. However, the detailed map result still depended on the correctness of the seed points. Unsupervised strategies without any human involvement have attracted a lot of attention in recent years. There are several kinds of unsupervised inundation mapping methods. Besides the unsupervised threshold, unsupervised feature extraction methods have been utilised. [PERSON] et al. [20] combines the pre- and post-flood images and apply the independent component analysis (ICA) to them. Segmentation and threshold are used to extract the flood from the change components. The cloud and crop components help to refine the maximum flood extent. In the work by [PERSON] et al. [21], the multi-temporal NDWI images are composited into one file.