仿生計算在水稻田影像判釋的差異性研究

近年來科技日益進步，遙測技術也不斷的改良與精進，應用衛星影像於處理大範圍面積的地表判釋也越趨廣泛。因此，如何有效運用衛星影像提升地表判釋的精度，便成為當前遙測技術持續發展的重點之一。過去對於水稻田的判斷都是以實地探勘方式進行耕地坵塊圖數化與編修，這些過程通常需要大量人力、物力與時間，然而透過遙測影像與資料探勘的分類技術，可以避免現場探勘之困境，並獲得合理可靠的結果。本研究使用遙測影像為素材，並隨機選取400樣本點，利用模糊粒子群聚類演算法獲取判釋規則，進行地表類別之判釋，所得正確率結果為80.75%，在同樣設計的研究中，使用蜂群聚類演算法及模糊粒子群演算法進行比較，結果發現蜂群聚類演算法較佳，其正確率結果為89.5%，所以此方法對於水稻田的判釋有良好的成果。另外研究中加入紋理資訊與植生指標作為輔助判釋的資訊，結果顯示可以有效提升判釋率，對於建構一套有效率之水稻田分類器有相當的幫助。

關鍵字

仿生計算；影像判釋；蜂群演算法；模糊粒子群演算法

並列摘要

Paddy-rice is an important food source in Taiwan, and government devotes a lot of resource to estimate annual food production. In the past, the paddy-rice field area estimation is obtained by field survey, which is both time and man-power consuming and requires a much higher budget. Land cover classification through remote sensing imagery and data mining technique is a well-accepted and popular approach for crop production estimation, thanks to the advances of remote sensing technology in recent decades. It is thus an important research topic that how to develop a methodology or algorithm which can improve image classification accuracy rate. In the present study an aerial photo over the Tanzi county of Taichung city was analyzed. Two different optimization algorithms, Artificial Bee Colony and Fuzzy C-Means Particle Swarm Optimization, were incorporated to the clustering analysis. The result indicates Artificial Bee Colony based clustering approach renders better classification accuracy rate than that based on Fuzzy C-Means Particle Swarm Optimization, which are 89.5% and 80.75% respectively. This study shows bionics computation is a feasible approach and alternative for remote sensing image classification.

並列關鍵字

bionics computation ； image classification ； artificial bee colony algorithm ； fuzzy C-Means particle swarm optimization algorithm

國際替代計量

仿生計算在水稻田影像判釋的差異性研究

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