台灣水庫多建造於山區，在夏季豪雨或颱風過後，容易發生山崩、土石流等自然災害，因此造成水庫周圍坡地的崩塌，嚴重影響水庫的蓄水與發電功能，若能建立有效的可能崩塌區域監控與判釋，對災害防治是一大助益。崩塌地的判釋傳統以現場探勘爲其重要的方法，然而這些過程通常需要大量人力、時間與經費，即使圖資完成，對於水庫周圍的崩塌現状，也往往不具有時效性。
爲能快速有效的判釋崩坍地，本研究採用模糊粒子群演算法(Fuzzy C-Means with Particle Swarm Optimization)分析，利用崩塌地之原始光譜因子並輔以植生指標，期望透過模糊分群法及群集粒子群優化法，能取得崩塌與非崩塌的判釋，再利用自我組織映射圖 (Self-OrganizationMap, SOM)法進行驗證，並交叉比對彼此優缺點，建構崩塌的判釋規則，以此規則在崩塌地的判釋影像判釋方面能提供快速判釋，有助於掌握崩塌發生之詮釋空間資訊，進而建置完整之崩塌地處理。

The reservoirs are generally constructed on the mountain area at Taiwan. The earthquake results in the soil distributed and typhoon will bring a huge amount of water to the reservoir zone. The movement of rock and soil of landslide into the reservoirs will produce soil deposit which influence seriously on the delivery of water. Accordingly， the landslide surrounding the reservoir will also dominate its life-time. The multi-scenario remote sensing data can effectively monitor the reservoirs. That is， the ancillary information is adopted easily by new technology. Few studies have been made to optimize the classification function.Unfortunately， the ancillary information requires to be examined to apply efficiently into the landslide decision system. The proposed method includes (a)Fuzzy-C-means + Particle Swam Optimization (FCM+PSO) can find the core factors (b)Self Organization Map (SOM) to construct the knowledge rule. Both of the classifier can approach about 84% of landslide image classification accuracy. Then the translation scheme of category amend is developed to enhance about 10% of accuracy.