China Institute of Water Resources and Hydropower Research

Juan Lv

Li Ni Zhang

Wei Jiang

Xuejun Zhang

Lihua Zhao

Hongjie Liu

Xiaotao Li

Yizhu Lu

Wenlong Song

Gan Luo

Xiaohui Ding

Rui Tang

Yuanyuan Duan

Zhicheng Su

Shiai Cui

Jingxuan Lu

Tengfei Long

Zhiguo Pang

Disclosed is a spatial simulation method for assessment of direct economic losses of a typhoon flood based on a remote sensing, and the method includes following steps: S, collecting a multi-source data before and after a typhoon flood disaster in an affected area and preprocessing the said data to obtain a preprocessed data; S, extracting reset parameters of a hazard-bearing body according to the preprocessed data and constructing a feature set of the reset parameters of the hazard-bearing body; S, collecting insurance disaster assessment data and historical investigation data in the typhoon flood-affected area, and constructing a sample set of direct economic losses of the hazard-bearing body; and S, carrying out a spatial simulation of the direct economic losses of the hazard-bearing body by adopting a deep learning of a neural network model according to the feature set of the reset parameters and the sample set of direct economic losses.

Spatial simulation method for assessment of direct economic losses of typhoon flood based on remote sensing

A Region Merging image segmentation algorithm based on boundary extraction is disclosed, comprising the steps of calculating a gradient image, extracting a boundary and carrying out initial segmentation and Region Merging, wherein each pixel is regarded as one region when initial segmentation is not conducted. In the Region Merging process, the portion of the common boundary of two adjacent regions that lies on the boundary image is taken as the merging cost, the regions are merged according to the ascending order of the mean gradient value inside the region, and a texture difference evaluation mechanism is introduced to reduce the wrong segmentation. The algorithm solves the problems of the other current segmentation algorithms, such as over-segmentation, easy to be influenced by noise and illumination, large in computation and memory consumption, in need of a large number of samples being marked manually and the like. Besides, all regions or all categories achieve the best segmentation result on one final segmentation result. These advantageous features can reduce the computational resource consumption of subsequent tasks and improve their results.

Region merging image segmentation algorithm based on boundary extraction

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Beijing, China

Juan Lv