基於整體學習演算法於航攝正射影像之物件分類探索－以海岸廢棄物為例

The Study of Orthophoto Image Classification through Ensemble Learning Based on the Aerial Photos: A Case Study on Coastal Waste Image Classification

10.6574/JPRS.202109_26(3).0004

劉奕洋(Yi-Yang Liu)；萬絢(Shiuan Wan)

整體學習 ； 影像物件分類 ； 資料空間視覺化 ； Ensemble Learning ； Image Object Classification ； Visual Data Space

航測及遙測學刊

26卷3期（2021 / 09 / 01）

181 - 192

繁體中文

近年臺灣在氣候變遷及海洋垃圾成長的環境下，多樣化的海岸樣貌正遭受威脅，在海岸廢棄物的議題上刻不容緩。有鑑於當今GIS的蓬勃發展，及遙測技術能夠在短時間內擷取大範圍的量化資訊，能看到有越來越多取代傳統調查方式的實際應用。本研究屬遙測技術結合機器學習的應用，以整體學習（Ensemble learning）之模型訓練方式在海岸廢棄物上進行物件分類實作的可行性，使用隨機森林（Random Forest）演算法及極限樹（Extra Trees）演算法所建構之模型，探討其分類成效差異，將機器分類後的數據將資料空間視覺化以繪製出主題圖。研究成果可應用於海岸廢棄物相關環境保育作業上，將為一經濟且有效的解決方案。

Presently, coastal features of Taiwan are threatened under the environment of climate change and the growth of the marine waste. The issue of coastal waste cannot be delayed any more. The most traditional method of disposing of coastal waste is to organize beach cleaning activities by coastal government agencies. That is, the private environmental groups using a large amount of manpower to manage the coast. The comparison of time cost and implementation efficiency is not ideal. In view of the development of GIS, the ability of remote sensing technology can capture a wide range of data in a short period of time. On the other hands, we can see many practical applications that replace traditional survey methods. This research is based on the application of remote sensing technology combined with machine learning to display the observation of our seashore. In this study, the Ensemble learning model is used to implement object classification. More specifically, Random Forest (RF) algorithm and Extra Trees (ET) algorithm are applied to explore different algorithms. The classification effects of the model constructed by established process are different. The data for the machine learning classification is visualized by the thematic map. The contributions can be applied to coastal waste related environmental studies effectively and practically.

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