建立航測影像稻作坵塊物件生成與時空變化模式之研究

Paddy Rice Objects Generating and Mapping Model Using Aerial Digital Image Data

10.6574/JPRS.2013.17(2).2

雷祖強(Tsu-Chiang Lei)；萬絢(Shiuan Wan)；黃政翊(Cheng-Yi Huang)；李哲源(Che-Yuan Li)；歐陽志豪(Chih-Hao Ouyang)

農業資訊管理 ； 水稻萃取 ； 最大概似分類器 ； 熵分類器 ； Agricultural Information Management ； Paddy Rice Extraction ； Maximum Likelihood Classifier ； Entropy Base Classifier

航測及遙測學刊

17卷2期（2013 / 08 / 01）

95 - 114

繁體中文

水稻為台灣主要糧食作物之一，政府每年投入大量資源進行水稻坵塊面積調查，調查成果將進行農業管理上之依據。但調查過程多以人工圈選方式進行向量圖檔之建立，此過程耗時甚鉅，因此如何快速地從高解析度數值航照影像中獲取地理空間訊息，進而達到農業資訊化管理之目的，基本上就是一個重要的研究課題。本研究欲發展一個結合坵塊尺度資訊與熵分類器（Entropy Base Classifier, EBC）的區塊物件化熵分類器（Region Object-oriented Entropy Based Classifier, ROEBC），並與傳統逐像元概念之最大概似分類器（Maximum Likelihood Classifier, MLC）來進行比較。研究成果顯示，使用ROEBC分類器可在各別波段產生切割點後並計算出個別屬性的資訊增益（Information Gain, IG）值。並且可以使用IF...THEN法則成功的將複雜的影像資訊的分類問題，轉化成一階邏輯概念的程序表達形式，這個形式會比傳統的統計參數式分類法則（如MLC法），更有效的表達出影像之知識內涵。此外物件導向分類器就點檢核與面檢核的觀點來看，均同時顯示優於逐像元式分類器之成果，這也顯示本研究所使用的物件導向分類器在製作水稻主題圖上，要比傳統逐像元的方式更為理想與成功。最後，本研究以坵塊化概念呈現了本研究成果與其他GIS資料整合分析的可行性，以及同時提供了轉作休耕田與外業調查人力投入度策略之分析成果。

Paddy rice is one of the major food crops in Taiwan. The government investigated the paddy rice area through aerial photography every year. However, paddy rice thematic maps require using much manpower which is very time-consuming and funding. How to quickly obtain this geo-information through very high-resolution aerial photographs is an important research issue. In this study, we want to develop a novel decision model (Region Object-oriented Entropy Based Classifier, ROEBC) which integrates the patch-scale information and Entropy Base Classifier. It also compares these results of traditional concept of pixel-based classifier by Maximum Likelihood Classifier (MLC). The ROEBC categories decision model can find the ideal cutting point from each spectral band through the value of attributes on Information Gain (IG). Based on these IG values, we can obtain rules from image information successfully. This method can clearly show the differences on image knowledge rule content by the traditional statistical parameter classifier (such as MLC). After that, in this study, we check the point accuracy and area accuracy at paddy rice thematic maps with MLC and ROEBC methods, respectively. It shows that the regional based classifier of ROEBC methods is better than those of the pixel based classifier of MLC. Finally, this study discussed the feasibility of paddy rice object results combine with other GIS data on the agriculture information management issues of future projects.