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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/5029


    Title: 應用空間相干法在氣候模式之地物分類研究;Applying spatial coherence method to landcover classification of meteorological model
    Authors: 陳盈真;Yin-Chen Chen
    Contributors: 大氣物理研究所
    Keywords: 遙測;衛星;空間相干法;地物分類;landcover classification;spatial coherence;remote sensing;satellite
    Date: 2008-07-09
    Issue Date: 2009-09-22 09:43:23 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 由於地表物種的分布狀態與地球能量收支平衡及水文循環等作用息息相關,在氣候模式中是重要的初始場參數之一,因此準確且有效的地物分類,將有助於氣候預報模式等相關研究之發展。 空間相干法( spatial coherence method )一向被應用於處理紅外光波段的衛星影像,主要為區分晴天視場與陰天視場,將雲和晴空海域區隔出來,其主要優點為濾除影像中部分有雲之混合像元(mixing pixel)。混合像元在應用衛星影像於地物分類的過程中,亦為誤差的來源之一,尤其在地物分布複雜的地區。因此,本研究將根據空間相干法的優點,濾除地表覆蓋物的混合區域,求得研究試區中各純種地物的光譜特性,應用於衛星影像之地物分類。空間相干法在建立物種光譜特性的過程中,將配合監督式分類法中的最大似然法,協助制定各類物種之光譜範圍最佳標準差,亦即結合非監督式與監督式分類法的優點,改進其共同的缺點──受混合像元之影響。 研究結果顯示,空間相干法在地物分類應用上有不錯的成效,與最大似然法之分類結果相當,特別是配合高解析度的SPOT5衛星觀測資料,可將道路明確從都會區中分出,顯示此分類方法具極高之可行性與潛力,非常適合應用於地物種類複雜的地區,將可成為地物分類應用的新方法。 Because of being strongly related to the heat budget of the Earth, hydrologic cycle, and as an important input parameter for climate models, an accurate and effective algorithm for landcover classification could make a great contribution to the climate model and environmental change researches. The spatial coherence method is originally developed to distinguish cloudy sky pixels from clear sky pixels all the time via satellite IR images, which is ascribing its key advantage of filtering out the mixing pixels (i.e. partial cloudy pixels) to get the pure values of certain surface covers (i.e. clear sky pixels or total cloudy pixels) under radiometric considerations. It is well known that the mixing pixel effect is one of main error sources in the classification tasks. Therefore, in this research the spatial coherence method is specifically applied as an alternative way in classifying landcovers, filtering out the mixing pixels to get the pure spectral characteristics of certain landcovers. This classification method seeks the assistance of the maximum likelihood method to pick up the best standard deviation, meaning that the method combines the advantages of the supervised and unsupervised classification methods. Result shows that a comparable performance to the maximum likelihood method does could be observed. Applying this method with the high-resolution SPOT5 images, it shows that the road pixels can be competently extracted from urban areas, revealing its high practicability and capability for typical Taiwan landcover patterns, and making it become a brand-new landcover classification method.
    Appears in Collections:[大氣物理研究所 ] 博碩士論文

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