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.
Highlights
To provide reliable and effective information for urban management and emergency decision-making, urban rainfall-flood model should first be based on ensuring the accuracy of data
The image data of Unmanned Aerial Vehicle (UAV) on the underlying surface of Zhengzhou University are pretreated in Section 2.2 of this chapter, and the Fuzzy C Means (FCM) clustering algorithm and MKFCM clustering algorithm were modeled by MATLAB
The parameters of the model were optimized, and the gradient descent method was used to select the relevant parameters of the FCM model and MKFCM model, and the set of optimized parameters was obtained
Summary
To provide reliable and effective information for urban management and emergency decision-making, urban rainfall-flood model should first be based on ensuring the accuracy of data. With the development of remote sensing technology and remote sensing image processing technology, it is possible to quickly extract the underlying surface information of urban, such as Quick-Bird, IKONOS, SPOT, WorldView and other high-resolution images. These images can be used for target recognition, they are susceptible to cloud and fog and have a long imaging period. Besides building algorithm KIT2FCM to overcome some drawbacks of the conventional FCM and taking advantage of fuzzy clustering technique on the interval type 2 fuzzy set in handling uncertainty, Nguyen [15] introduces combining the different kernels to construct the MKIT2FCM, which provides us with a new flexible vehicle to fuse different data information in the classification problems. Geo-Inf. 2019, 8, 205 ISPRS Int. J.
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