1. Introduction
Flood disasters have the characteristics of wide coverage and strong suddenness, especially during wet seasons or rainy weather [
1]. Affected by natural and human factors, floods have been one of the most frequent natural disasters in the past two decades [
2]. Although the average number of deaths per flood disaster has decreased with the development of technology, the number of floods is increasing every year, which poses a great threat to human safety and property. How to accurately monitor the flood lifecycle has become an important part of emergency disaster management and rescue guidance [
2,
3].
As core tasks in flood mapping, the accurate extraction of water body data and the detection of water changes have been long-standing research hotspots [
4,
5]. The traditional methods obtain the water body information using manual field investigation or hydrological monitoring stations. Although these methods can achieve high accuracy, they can only perform point measurements with small coverage and require a significant amount of manpower and resources. In flood emergency monitoring, the on-site investigation method is usually difficult to rapidly apply in the affected area, while the monitoring stations method has difficulty reflecting the overall situation, making traditional methods inadequate in flood monitoring.
Thanks to the continuous development of satellite sensors, Earth observations from multi-source satellites provide the possibility of near real-time flood monitoring and more effectively support emergency management than gauged data [
6]. As advanced earth observation means, both active and passive satellite remote sensing data can be widely used for inundation mapping at different stages of flood occurrence [
7,
8,
9]. Due to the advantages of visualization and the interpretability of images, optical sensors can be used to obtain water surface data straightforwardly and reliably. With the accumulation of long-term and high-resolution satellite data, many optical remote sensing approaches contribute significantly to the delineation of global and regional water surfaces [
10,
11]. As flooding is often accompanied by extreme weather, passive optical sensors are frequently affected by clouds and unclear weather conditions, resulting in reduced effectiveness during flood events. Alternatively, active microwave sensors, especially the synthetic aperture radar (SAR), provide their own illumination of the Earth’s surface and thus can observe day and night, as well as through cloud cover [
12], which is very suitable for monitoring during and after flood events [
13,
14,
15]. SAR remote sensing technology is suitable for information extraction of the flood lifecycle because of its advantages in large-scale, all-weather, and all-day and -night monitoring.
Considering the characteristics of water bodies in SAR imagery, the radar backscatter of open calm water bodies is usually lower than the surrounding terrain, which makes it possible to detect floodwater in SAR imagery [
6,
16]. In general, the interaction between electromagnetic waves and calm water surfaces is mainly manifested as odd scattering, which makes the region of water dark in radar imagery. Moreover, the physical scattering of water bodies in SAR imagery is significantly different from land areas, which can be distinguished by several different kinds of approaches [
17,
18]. Among these methods, the thresholding method [
19,
20] is the most commonly used approach. Its application for water extraction is rather straightforward: a global threshold is applied to the whole scene, allowing the water to be separated from other land covers. The threshold can be selected by visual inspection of the backscatter histogram or by using automatic approaches [
21]. Accurately selecting thresholds is vital to improve the accuracy of the flood mapping. Numerous automatic threshold selection methods have been developed [
22,
23,
24], among which the Kittler and Illingworth’s minimum error thresholding (K&I) [
25] method and Otsu’s maximum between-class variance [
26] method are widely employed because of their simplicity and generalization. The K&I algorithm is based on Bayesian theory, and it determines the optimal threshold by minimizing the classification error. The Otsu algorithm calculates the between-class variance of the grayscale values for every possible threshold and determines the optimal threshold by maximizing the between-class variance.
Thresholding-based methods have the advantages of simple calculation and less time consumption while yielding competitive accuracy [
27,
28]. However, some other objects with low backscattering, such as farmlands, roads, and airstrips, are prone to interfere with the water extraction results, leading to over-detection in flood mapping. This phenomenon is caused by the same backscattering characteristics of different objects, which is difficult to overcome by a single temporal SAR image. By utilizing temporal information and comparing images from different periods, stable targets such as roads and shadows can be effectively removed [
29]. Furthermore, floodwater can be distinguished from permanent water bodies by analyzing the decrease of backscattering caused by flooding-related compared to non-flood reference images [
13,
30]. Therefore, the change detection technique using multi-temporal information can map the flood by detecting the changes in floods.
Based on whether using a priori knowledge or sample information, the change detection approaches involve supervised and unsupervised approaches [
31,
32,
33]. With the development of artificial intelligence technology, supervised change detection methods based on machine learning and deep learning technologies [
32,
33,
34,
35] are widely used. However, these methods need massive and high-quality samples, which are not suitable for rapid flood mapping of emergency responses. On the other hand, the unsupervised change detection methods are simple and efficient and can detect the flood regions without a priori knowledge or samples [
34,
35,
36]. Considering the emergency needs of flood mapping, the unsupervised change detection methods are chosen in the proposed framework.
In general, unsupervised change detection involves several steps, including data preprocessing, the generation of difference images, and the extraction of changed regions. In the step of data preprocessing, multi-temporal SAR data are processed by radiometric calibration, co-registration, and speckle filtering. After obtaining accurate preprocessing results, many methods for difference image generation were designed for different polarizations, such as log-ratio [
37,
38], neighborhood-based ratio [
39], and feature fusion [
8,
9] for single/dual polarization SAR data, as well as test statistic [
23] and Hotelling–Lawley trace statistic [
40] for fully polarized SAR data. Despite preprocessing, such as filtering, some noise may still exist in the difference image. In this situation, image segmentation methods [
41,
42,
43] can be used to further suppress the impact of speckle noise. This type of method divides the image into multiple homogeneous regions, thereby reducing the false alarm rate of detection and improving the processing speed. In the step of changed region extraction, a number of methods have been developed for accurate and efficient automatic threshold selection, such as the K&I algorithm [
25], Otsu [
26], entropy thresholding [
44], et al.
In order to overcome the limitations of using a single image and a single algorithm, more complex frameworks that integrate multi-image and multi-algorithm methods have been proposed [
45] to achieve higher accuracy in flood mapping. Although these algorithms work well, few of them have been widely used in practice. On the one hand, some algorithms are relatively complex and rely on ground auxiliary data, making them difficult to understand and implement. On the other hand, some algorithms have low execution efficiency and can only obtain the ideal result by adjusting the parameters multiple times through the trial-and-error method. In this paper, we focus on the design of the flood mapping algorithm with the following characteristics: (1) accurate—both the false alarm rate and missing alarm rate are low, and the boundary between water and land is as accurate as possible; (2) efficient—the final result can be achieved as quickly as possible without the need for multiple trials and errors; (3) simple—both the principle and workflow are as simple as possible in order to facilitate implementation and operation by users.
Based on the above principles, we propose a rapid flood mapping framework to deal with sudden flood disasters in a short time. The main innovation is reflected in three aspects: First, a semi-automatic thresholding algorithm for preliminary flood extraction is proposed, which does not require parameter setting and multiple iterations. Second, a change detection algorithm considering neighborhood information is proposed to extract inundation areas while excluding permanent water bodies, which can effectively suppress the speckle noise. Third, the final flood map is extracted by combining the preliminary inundation map and the change map through a simple intersection strategy, which not only improves the accuracy of flood extraction but also ensures the efficiency of the algorithm.
This paper is organized as follows:
Section 2 introduces the proposed method and evaluation criteria. The study areas and datasets, experimental results, analyses, and discussions are presented in
Section 3. The factors affecting the effectiveness of change detection were discussed in
Section 4. Finally, conclusions are drawn in
Section 5.
4. Discussion
In order to display and analyze the result obtained by our method more clearly, the comparison map between the flood map and the ground truth map is shown in
Figure 12. From the map we find that the missed detection area in the black dotted ellipse on the upper left is caused by change detection. The quality of change detection is not only affected by the principle of the algorithm but also largely depends on the selected reference image. The selection of the reference image in this paper adheres to the following criteria: (1) the same or similar imaging parameters, such as band, polarization mode, incident angle, etc.; (2) close imaging time to ensure similar vegetation growth and climate conditions.
The missed detection area in the dotted ellipse on the upper right shows a regular polygonal outline in
Figure 12. This patch was marked as inundated terrain in the ground truth map. However, no ground objects with the water characteristics were found in the same position of the flood image (
Figure 4b). This may be due to the ground truth map coming from different sensors and being obtained by the semi-automatic method [
4]. This area is also included in the calculation of MAR. If this block of water is removed, the MAR will decrease.
In addition to the missed detection, there are some false alarms in
Figure 12, as shown in the rectangular boxes. Among them, the false alarm regions in the small rectangle are larger than those in the big rectangle. By comparing the three images in
Figure 4, it can be seen that these regions are low backscattering regions of change, whose characteristics are consistent with floods, so they are recognized as floods. The false alarm regions in the big rectangle are smaller and broken and also are low backscattering regions of change. However, due to their small areas, it is difficult to determine whether the changes are caused by rain puddles or by noise. Although these false alarms can be filtered out by segmenting the flood image and setting a minimum segmentation size threshold, we choose to retain this information in the case of uncertainty. On the one hand, the emergency managers generally pay more attention to the large flood areas than the small broken areas. Therefore, these small areas have little impact on flood mapping applications. On the other hand, these small areas may be puddles caused by rainfall, and their quantity and distribution may also help managers make decisions.