以遙測對水稻田影像判釋之研究:螞蟻聚類演算法為實證

稻田在都市環境上對水土保持之影響主要有三: 區域性水田綠化環境、水田調洪功能與二氧化碳的吸收淨化空氣。在台灣水稻為台灣賴以維生的糧食作物，為了讓政府取得糧食政策制訂、產量推估與農民休耕或災後補助之依據來源，通常都會進行水稻種植面積的調查。而以往對於水稻田的判斷都是以實地探勘方式進行耕地坵塊圖（Rice Pattern）數化與編修。這些過程通常需要大量人力，因此透過遙測影像與合適的分類器足以改善現場探勘之困境。本研究的主旨在以螞蟻聚類演算法方法進行分析影像分析其目的在改善聚類優化的問題。此方法的優點在運算的過程簡單且為非監督式方法不需資料類別其步驟詳細的說可分為(a)初使螞蟻個數 (b)計算螞蟻選擇群聚 (c)更新費洛蒙 (d)計算新群心(e)進行迭代以此方法得到的結果為正確率為80.44% 並以傳統的支援向量機進行比較，若以計算成本來說確有不錯效果，故此法有助於水稻田的影像分類問題的解決。

關鍵字

影像分類；螞蟻聚類演算法；模糊粒子群演算法；紋理資訊；植生指標

並列摘要

Paddy rice is the major crop of food in Taiwan. There are three main contributions on Taiwan: regional eco-friendly of environments, adjustment of floods and refresh the air pollution. The estimation of area is important since this information are related to the national food policy, yearly crop yields calculation and post-disaster reimburse. In the past, it is performed through field exploration and rice pattern revising, which is a large amount of human power and time-consuming works. A more economic manner to estimate paddy rice area is desired. Accordingly, this study aims to design a paddy rice classifier in which the area of paddy rice in a remote sensing image can be calculated. In this study, the ant-clustering algorithm is developed to paddy rice image classification. The advantage of this algorithm is to effective cluster data into groups for unsupervised learning.The advantage of this algorithm is to effective cluster data into groups. The steps are (a)initialize the number of ants (b)compute the ants to each cluster center (c)renew the pheromone (d)recalculate the cluster center (e) do iterations. The accuracy rate of this method is 80.44% of landslide classification. The landslide thematic map is drawn and the position of occurrence place is shown. The advantage of this process is low cost and it is very effective comparing to other supervised learning approaches.