朱子豪臺灣大學:地理環境資源學研究所劉威廷Liou, Wei-TingWei-TingLiou2010-05-052018-06-282010-05-052018-06-282008U0001-1906200802092500http://ntur.lib.ntu.edu.tw//handle/246246/179679　　現行土地利用調查之影像分類，有時需要超過95%之分類準確性，然而一般自動化影像分類後之準確性平均而言為85%左右，用人工判釋可達100%的準確性，但卻是極為耗費人力且不切實際的做法，因此如何在自動化分類後提升至要求的準確性即是本研究的主要目的。　本研究的誤差分析是利用像元經過最大相似法分類後，產生之類別機率值作為誤差判釋的基礎，像元類別機率值過低或過近是發生誤差的主要原因。因此研究方法運用誤差分析平台將像元的前三高類別機率值寫出，並經由閾值的設定找出可能發生誤差的像元，透過均質區的套疊，進行人機互動判釋以補足其準確性。　研究結果顯示在誤差分析概念上，像元類別機率值過低或過近的確是誤差發生的主要原因，但要注意影像的陰影與植生變異是降低分類準確性另一主因，且是不適用於誤差分析概念；在誤差分析平台上，透過閾值的設定，將有誤差類型的像元套疊至均質區中，經由人機互動方式補足準確性，是一種改進全自動與全人工之間－「全有」、「全無」概念的新做法。　　While the accuracy for interpretation of satellite images in land-use surveys is required to exceed 95% in some cases, automatic classification of such images can only reach an average accuracy level of about 85%. Although artificial interpretation can secure perfect accuracy of 100%, it is too labor-consuming to be practical. This thesis focuses on how to increase the accuracy for using auto-classification to a required level. 　Using a maximum likelihood method, satellite images are automatically classified into several types of land cover, each with a computer-determined probability. For the probability for a type of land cover, the lower or the closer to the probability for another type of land cover, the more likely an error in automatic interpretation. This is an error analysis platform used in the thesis.　For an image, three types of land cover with the top three probabilities are selected for error analysis. For a type of land cover, if its probability is lower than a specified critical value or the difference between the probability and another probability (for another type of land cover) is lower than another specified critical value (the two probabilities are too close), artificial interpretation through overlapping picture elements on homogeneous areas is made to find whether the auto-classification is correct.　Using the error analysis, it is found that too low probabilities for types of land cover or too close probabilities between two types of land cover are the main reason behind the interpretation error due to auto-classification. However, shades in images or variation in vegetation also account for such errors and the two factors are not suitable for being incorporated into the error analysis.摘要 ⅠBSTRACT Ⅱ一章 緒論 1一節 研究動機與目的 4二節 研究範圍與內容 6二章 文獻回顧 9一節 遙測影像對於植生判釋相關研究 7二節 遙測影像分類準確性相關研究 9三章 研究方法 15一節 研究方法與架構 15二節 植生指數 16三節 分類理論 19四節 影像分類準確度評估 24五節 誤差成因分析與處理方法 29六節 資料套疊 32四章 研究成果與討論 34一節 研究素材準備 35二節 第一次準確性評估 38三節 誤差分析與閾值設定 44四節 均質區套疊與人機互動 48五章 結論與未來研究 51一節 結論 51二節 未來研究 54考文獻 56application/pdf3633891 bytesapplication/pdfen-US分類準確性最大相似法誤差分析像元類別機率值accuracyMaximum Likelihood classifiererror analysispixel probabilitythesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/179679/1/ntu-97-R93228014-1.pdf