在台灣，使用福衛二號影像對災害監測之崩塌地辨識相對於其他衛星有幾點優勢，例如：收費低廉、再訪頻率高、全島拍攝等等。但是，福衛二號影像僅含四個光譜頻帶，這樣有限的資訊對於崩塌地自動判識是不能滿足實際上的需求，例如：崩塌地與細支流部分常互相混淆，造成誤判。本研究中，我們站在分類技術角度上來克服福衛二號影像光譜資訊不足的困難。
首先，為了增加崩塌地與其他類別間的分離度，我們試圖擷取更多具鑑別度的特徵，像是紋理與地形上的特徵。紋理特徵是利用對數極座標-小波包轉換，從福衛二號影像中的光譜資訊萃取而得；另外，地形坡度特徵則是從數值高程資料獲得。再者，我們利用一個情境式分類器結合光譜與空間資訊對於特徵相似物的分類。利用數個實地勘察的區域來分析辨識實驗結果，顯示我們的方法是可行的並且得到明顯的改善。

To identify landslides for disaster monitoring, FORMOSAT-2 imagery has the advantages of low cost and frequent revisit over any other satellite imagery currently available in Taiwan. However, the images with four spectral bands are not capable enough to distinguish landslides from other ground cover types, such as small river channels. This study proposes to overcome the problem of spectral incapability using the following techniques. First, we explore more discriminative features, such as texture and topographical features, in order to improve class separability. Texture features are extracted from the FORMOSAT-2 imagery itself using the log-polar wavelet packet transformation. A topographical feature ‘slope’ is derived from an auxiliary Digital Elevation Model (DEM) dataset. Second, we employ a contextual classifier because combining spectral and spatial information is helpful for homogeneous object identification. Field investigation has been conducted on several sites to validate the result of analysis. Experiments show the feasibility of our approach to landslide identification based on multispectral imagery.

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