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| 研究生： |
盧安里 Ruli Andaru |
|---|---|
| 論文名稱： |
以長廊帶沙洲地形變遷觀測與火山熔岩流災害區製圖
驗證一個多時期無人機影像之套合策略 A multi-temporal UAV images co-registration strategy evaluated by the observations of long corridor sandbank morphological changes and mapping of volcanic lahar hazard areas |
| 指導教授： |
饒見有
Rau, Jiann-Yeou |
| 學位類別： |
博士 Doctor |
| 系所名稱： |
工學院 - 測量及空間資訊學系 Department of Geomatics |
| 論文出版年： | 2022 |
| 畢業學年度： | 110 |
| 語文別： | 英文 |
| 論文頁數： | 112 |
| 中文關鍵詞： | 形態變化 、基於圖像之套合 、⻑廊沙洲 、活躍之熔岩穹頂 、潛在火山泥流災害 |
| 外文關鍵詞： | morphological changes, image-based co-registration, long corridor sandbank, active lava dome, potential lahar hazard |
| 相關次數： | 點閱：83 下載：3 |
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多時態之無人機（UAV）影像融合為UAV攝影測量相關應用之必備先決條件，其主要被用於降低不同時期之影像錯位現象。藉由攝影測量技術所處理之多時態UAV影像精度與影像品質、相機模型、影像融合策略、飛航規劃以及相機知地理定位有所相關，基於前述之各類因子，勢必需建置精確之影像融合方法、合適之相機模型、分佈良好之共軛點（地面控制點，GCP）以及精確之影像地理標記位置。然而，真實物體之表面紋理以及大氣現象可能產生不精確的三維表面重建成果，導致像空間中缺乏明確之特徵點以及空間分佈，進而造成影像匹配之不穩定性。而GCP設置位置之可達性也增加了GCP測量之困難度，此外，多數UAV僅配備消費等級之設備（GNSS及相機），其無法提供精確之影像位置，導致影像及後續製成之數值地表模型（DSM）產生幾何面向的畸變。由於較弱的成像幾何結構以及相機律定後剩餘之系統性影像詞差，導致畸變現象通常在線性之飛行方向上增加，例如沿海沙洲，
在本研究中，我們為解決多時期無人機影像匹配問題，其中包含了多項改進辦法進而不需要整個影像資料集之地理標記資訊，甚至不需要在狹長型之測試區內部設地面控制點 (GCP)。首先，第一項改進中，我們使用兩步驟半開放式自行率定法(semi-on-the-job self-calibration, OTJSC)，其利用具有方向及飛行高度變化預先率定相機之內方位參數及附加透鏡畸變參數，以解決UAV影像幾何失真問題，隨後以另一個semi-on-the-job self-calibration的內方位參數以標記影像位置。再者，利用對比度限制質方圖(CHLAE)以增強影像以利提取影像特徵及連結點。而後，基於影像匹配(image-based co-registration, IBC)提出了一項同時匹配多時期影像之方法，稱為移轉空中三角測量(Trans-AT)，其因能最小化地表模型之幾何錯位而被提出，透過結合兩個連續航帶之帶狀UAV影像圖塊(strip image block, SIB)之空中三角測量過程(其類似於GNSS輔助空中三角測量)。最後的改進辦法中，以機器學習中的隨機森林(random forest)進行因影像匹配錯誤產生之點雲雜訊移除，尤其位於水體、雲層以及火山口。而隨機森林也被使用在物體分類及移除非地面之物體方面，以產製後續監測地表變化所需之數值地面模型(DTM)。
為了評估本研究所提出的改進策略，選用了兩個研究地點，即台灣西南部的長廊沙洲和印尼峇厘島阿貢火山活躍的熔岩穹頂。實驗結果表示，研究提出的改進策略可提供最佳擬合相機參數，提高了圖像匹配的可靠性，並最大限度地減少套合 DTM 時垂直方向的差異。通過 Trans-AT 的 8 個步驟，可以正確套合9個不同時期的無人機沙洲數據集。該方法實現了高精度結果，內陸區域的平均垂直RMSE為13.5 cm，沙區為17.2 cm，相較於無人機影像之空間解析度低了1.5個像素。而在阿貢火山的數據集，需要結合 IBC 和點雲對點雲（cloud-to-cloud, C2C）之匹配技術以及粗略和精細的套合過程來共同套合六個時間序列的數據集。對於IBC和C2C的套合成果，垂直方向之RMSE分別為0.58 m 和 0.72 m。我們進一步使用套合的 DTM 來評估沙洲表面和熔岩穹頂在不同時間序列的形態變化。此外，我們提出了一種用於觀察火山泥流災害區域（淹沒區）的自動化工具，借助LAHARZ (USGS) 程式以緩和噴發後潛在的二次火山泥流效應。
Multi-temporal unmanned aerial vehicle (UAV)-images co-registration is an essential prerequisite for any further UAV photogrammetric application to minimize errors associated with misalignment at the same areas captured on different dates. The accuracy of multi-temporal UAV through photogrammetry processes depends on several variables, including visual image quality, appropriate camera model, co-registration strategy, flight designs, and geo-localization of camera positions. To achieve accurate co-registration, an appropriate camera model, well-distributed tie-points/ground control points (GCPs) and accurate geotagged image positions are required. However, the natural texture of the object’s surface and the atmospheric phenomena can cause image matching uncertainty due to insufficient distinct key-points and spatial distribution in the image frame toward inaccurate 3D surface reconstruction. The site accessibility also makes the measurement of GCPs difficult or even impossible. Moreover, most UAVs are equipped with consumer-grade devices (onboard GNSS and non-metric cameras)—they cannot provide accurate image positions, leading to geometric distortions of the image network and the generated digital surface model (DSM). These distortions generally increase in a linear flight direction (corridor surveys), such as coastal sandbank due to weak imaging geometry and remaining systematic image residuals after camera calibration.
In this study, we address the co-registration of multi-temporal UAV images incorporating several improvements without the need for accurate geotagged information on whole dataset and even distributed GCPs over a corridor-shaped area. In the first improvement, we perform a two-step semi-on-the-job self-calibration (OTJSC) to overcome the problems in the geometric distortion of image network using UAV image datasets with favor orientations and flight altitude variations to pre-calibrate the interior orientation parameters (IOPs) and additional lens distortion parameters (APs). This was then followed by another OTJSC of IOPs involving images with accurate geotagged positions. Second, contrast limited adaptive histogram equalization (CLAHE) image enhancement is applied to distinguish image features and increase the number and distributed of extracted tie-points. Third, a method to co-register multi-temporal UAV datasets through image-based co-registration (IBC), called “transferred aerial-triangulation (Trans-AT) is proposed to minimize the geometric misalignment of the generated surface models. It conducted by performing AT procedure (similar to GNSS-supported AT) of a combined two consecutive UAV datasets within a strip image block (SIB). In the last improvement, we applied a random-forest (RF) machine learning to remove noise points among the generated dense point clouds due to image matching failures, mainly caused by water bodies, clouds, and fumarole objects. The RF was also used to classify and remove non-ground objects, resulted in digital terrain model (DTM) as representation of bare-earth surface. The reconstructed surface models were then used for observing morphological changes and potential hazard.
To evaluate those proposed improvement strategy, two case study sites were chosen, i.e., long corridor sandbanks in southwestern Taiwan and Mt. Agung’s active lava dome in Bali, Indonesia. The experimental results reveal that the proposed improvement strategy provides the best-fit camera parameters, improve the image matching reliability, and minimize the vertical discrepancies of co-registered DTMs. Through eight steps of Trans-AT, nine epoch UAV sandbank datasets can be co-registered properly. It achieves high accuracy results, with an average vertical RMSE of 13.5 cm in the inland areas and 17.2 cm in the sand area, which are lower than 1.5 pixels of the UAV image spatial resolution. As for Mt. Agung datasets, a combination of IBC and a cloud-to-cloud (C2C) matching technique with a coarse and fine registration process are needed to co-register six time-series datasets. It achieved a vertical RMSE of 0.58 m and 0.72 m for IBC and C2C co-registration, respectively. We further used the co-registered DTMs to assess the time series of morphological changes of sandbank surface and lama dome emplacements. Additionally, we proposed an automated tool for observing the areas of volcanic lahar hazard (inundation zones), mitigating the potential secondary lahar after eruptions with the aid of LAHARZ (USGS) programs.
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