應用人工智慧模組於流域集水區之雷達極化判釋

10.6653/MoCICHE.202310_50(5).0004

陳奕中；陳柔妃；郭志禹

土木水利

50卷5期（2023 / 10 / 01）

18 - 26

繁體中文

自從人工智慧圍棋程式「AlphaGo」屢屢戰勝棋王後，類神經網路以及深度學習技術成為眾所矚目的焦點。深度學習在遙感領域的應用日益廣泛，以目標物識別、土地覆被分類以及地表變異偵測等研究為主，利用機器學習工具發展不同影像來源之影像數據訓練，以做為進行廣域快速地表特徵變異之利器。隨著全球氣候變遷、極端災害事件頻傳，臺灣位於亞熱帶季風區、活躍的造山帶且地質材料破碎，經年累月受到颱風及地震等之自然災害侵襲，劇烈的地表作用造成大規模崩塌災害產生。為此，學術界以及相關主管機關長期投入坡地崩塌事件資料蒐集、致災因子評估與地質模型建置等工作，累積颱風、豪雨事件前後大量地表變異資料，提供深度學習在自動化崩塌判釋最佳之範例。本研究以旗山溪集水區為研究範圍，整合ALOS雷達與福衛光學影像，利用卷積神經網路學習工具進行自動化崩塌判釋。以莫拉克災前後光達數值地形，搭配福衛二號影像與航空照片，進行潛在大規模崩塌地形特徵判釋。配合卷積神經網路工具進行研究區域已發生之大規模崩塌區位目錄，進行福衛2號大規模崩塌之影像數據訓練。此外，利用日本太空總署ALOS／PALSAR衛星雷達雙偏極HH和HV影像，強化目標物在不同偏極電磁波之辨識能力，配合前述福衛2號判釋成果以及崩塌前後量體建立山崩面積－體積經驗迴歸公式，進行旗山溪流域地表變異分析。本研究成果包括：（1）旗山溪流域福衛2號光學影像崩塌事件判釋成果與崩塌區位目錄；（2）旗山溪流域ALOS雷達影像崩塌事件判釋成果與崩塌區位目錄；（3）建立旗山溪流域歷次崩塌事件之卷積神經網路地表變異識別模式及圖像訓練資料庫。

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