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    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/106699

    題名: Flood Identification from Satellite Using Neural Networks
    作者: Chang, L. C.;Kao, I. F.;Shih, K. K.
    日期: 2011-12-05
    上傳時間: 2016-04-27 11:21:31 (UTC+8)
    摘要: Typhoons and storms hit Taiwan several times every year and they cause serious flood disasters. Because the rivers are short and steep, and their flows are relatively fast with floods lasting only few hours and usually less than one day. Flood identification can provide the flood disaster and extent information to disaster assistance and recovery centers. Due to the factors of the weather, it is not suitable for aircraft or traditional multispectral satellite; hence, the most appropriate way for investigating flooding extent is to use Synthetic Aperture Radar (SAR) satellite. In this study, back-propagation neural network (BPNN) model and multivariate linear regression (MLR) model are built to identify the flooding extent from SAR satellite images. The input variables of the BPNN model are Radar Cross Section (RCS) value and mean of the pixel, standard deviation, minimum and maximum of RCS values among its adjacent 3×3 pixels. The MLR model uses two images of the non-flooding and flooding periods, and The inputs are the difference between the RCS values of two images and the variances among its adjacent 3×3 pixels. The results show that the BPNN model can perform much better than the MLR model. The correct percentages are more than 80% and 73% in training and testing data, respectively. Many misidentified areas are very fragmented and unrelated. In order to reinforce the correct percentage, morphological image analysis is used to modify the outputs of these identification models. Through morphological operations, most of the small, fragmented and misidentified areas can be correctly assigned to flooding or non-flooding areas. The final results show that the flood identification of satellite images has been improved a lot and the correct percentages increases up to more than 90%.
    關聯: 2011 AGU Fall Meeting
    顯示於類別:[水資源及環境工程學系暨研究所] 會議論文


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