Improving Map-Based Post-Disaster Management Systems a Guassian Fusion Approach

Abstract

Providing full and accurate information is crucial to the post-disaster management to enable the affected people access and obtain the resources needed, in a timely manner, but, the current map-based post-disaster management system lack of providing the emergency resource lists without filtering them, as a result the post-disaster management system consumes high levels of time and energy in calculation. An effective post-disaster management system (PDMS) has to ensure distribution of emergency resources, such as hospital, storage and transportation in a reasonable time so that affected papulation are properly benefited from it during the post-disaster period. In the method proposed in this paper, first, initial mapping and disaster mapping was proposed under Gaussian transformation and the maps image acquired as histogram. And then, all the maps, which are under discrete wavelet transform (DWT), were converted as DWT images by applying Gaussian fusion algorithm. Second, inverse DWT (iDWT) is applied to generate a new map for post-disaster management system. Finally, simulations were carried out and the results evaluated in terms of the indices, namely entropy, spatial frequency (SF) and image quality index (IQI). The evaluation results show that the proposed method is more effective than the other fusion algorithms, such as mean-mean fusion and max-UD fusion.