臺灣博碩士論文加值系統

地形學和表面特徵辨視在對於防範預防天災上面是一個很重要的議題。它可幫助政府早點在地面上找出具有危險的地域。本論文提出一個航空影像圖片表面特徵辨識系統，它包含二大主要模組，分別為特徵擷取器和雜訊校正器。特徵擷取器將影像圖片壓縮並由其特徵值來判斷地面特徵。雜訊校正器利用模糊類神經網路來校正錯誤圖片分類。本系統應用於颱風災害預測，利用表面特徵辨識系統來預測颱風經過可能對台灣的農作物造成多大的傷害。

Topography and surface feature recognition is an important issue of natural disaster prevention. Correct feature recognition can find and predict possible damages of typhoon earlier for the government so the right rescue strategy can be taken. This work proposes an aerial photograph surface feature recognition system. The system contains two components, namely, feature extractor and noise reviser. Feature extractor uses wavelet transformation and support vector machines to conduct image reduction and feature recognition. Noise reviser uses fuzzy neural networks to eliminate misclassified patterns. The system is applied to the domain of crop damage prediction of typhoon. The experiments showed that the system can find important surface features from aerial photograph as well as predict possible typhoon damage correctly.

第一章 簡介 5
第二章 系統架構 8
2.1 特徵擷取器 8
2.2 雜訊校正器 12
第三章 表面特徵辨識應用於颱風災害預測系統 16
3.1 颱風災害預測 16
3.2 航空影像圖片表面特徵辨識系統 19
第四章 討論和結論 24
4.1 討論 24
4.2 結論 27

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