Obtaining Land Cover Type for Urban Storm Flood Model in UAV Images Using MRF and MKFCM Clustering Techniques

Abstract

With the accelerated urbanization process, cities are suffering from extremely heavy rain and urban storm water logging disasters in recent years. To provide reliable and effective information for urban management and emergency decision-making, the accuracy of basic data must be guaranteed in any urban rainwater model. This paper presents a novel MKFCM-MRF (Multiple Kernel Fuzzy C Means-Markov Random Field) model to segment high-resolution Unmanned Aerial Vehicle (UAV) images. The core ideas of MKFCM-MRF model are as follows. Firstly, in order to increase the correlation information between pixels, multiple-kernel functions are introduced into Fuzzy C Means (FCM) clustering algorithm, which automatically filters out the optimal weight combination among kernel functions according to the distribution characteristics of pixels in feature space. Secondly, in order to better segment the texture and edge of the image, the clustering results of multiple-kernel FCM clustering algorithm are introduced into Markov Random Field (MRF) model, a novel spatial energy function integrating fuzzy local information is constructed. Finally, based on the total of data and spatial energies, the raw clustering results are regularized by a global minimization of the energy function using its iterated conditional modes (ICM). The effectiveness of MKFCM-MRF is performed by high-resolution UAV images data. The experimental results indicate MKFCM-MRF can refine the classification map in homogeneous areas, while reducing most of the edge blurring artifact, and improving the classification accuracy compared with FCM clustering algorithm. In addition, the validation of the urban storm flood model shows that the trend of the two clustering results is the same, but the runoff producing time and the peak time of FCM clustering results are advanced, the peak flow and the total runoff are larger; the simulation results corresponding to MKFCM-MRF clustering results are more realistic.

Spatio-Temporal Visualization Method for Urban Waterlogging Warning Based on Dynamic Grading

“…Extreme precipitation events and the resulting urban rainstorms were first found in developed countries in Europe and America which had rapid urbanization, and then caused worldwide attention [7][8][9]. The occurrence of urban waterlogging is mainly caused by the following four reasons [10]: (1) Extreme heavy rain events; (2) Waterlogging in the outer river; (3) Changes in the underlying surface of the city affect the law of production and confluence; and (4) The planning and design of drainage systems are unreasonable.…”

“…Figure 12 shows the complete process of a rainstorm waterlogging lasting 10 hours in the study area, expressed in 0 h-10 h, and dynamic visualization in hours (Scheme 1 in color scheme selection Table 2): (2) Temporal granularity The performance of temporal granularity is based on the temporal granularity calculation model proposed in Section 3.4.2. In this system, the following aspects are more significant: 2 Switching time per frame. The switching speed of the animation is greatly constrained by the user's cognitive efficiency.…”

“…According to statistics, the loss caused by flood disaster alone is as high as 40% every year worldwide. Against the background of rapid urbanization, with new construction, reconstruction, and expansion projects changing the original surface conditions and increasing the impervious area, the problem of urban waterlogging has become more and more serious [1,2]. The rainstorm and waterlogging disaster will affect many aspects of the complex system of the city, for example, the road and its ancillary facilities will be damaged in the process of the rainstorm and waterlogging, resulting in the decline of its traffic capacity; once the traffic system is interrupted or paralyzed, it will have a very adverse impact on the production and life of the city.…”

Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review

“…This issue can be curtailed by using DSRM acquired through LiDAR technology. Moreover, Wang et al [77] have proposed a multiple kernel fuzzy C means-Markov random field (MKFCM-MRF) model for the clustering of images obtained from UAVs. The advantage of using the MKFCM model is the reduction in noise while keeping the edge detection information preserved and the automatic optimisation of the eigenvector distribution in space.…”