台江海岸環境與社會人文變遷:以空間資訊系統整合-以三維建物模型進行遙測影像幾何校正之研究
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2012/08-2013/07
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我國第一枚光學遙感探測衛星-福爾摩沙衛星二號自2004 年發射升空以來，以其每日重訪 及高空間解析度的優越性，已累積為數可觀之臺灣地區影像，可作為國土利用變遷之重要 參考資訊，此亦為本整合型計畫之發想緣起。然而衛星影像必須經過正確且高精度的幾何 改正，才能獲得有意義的影像判釋結果，也才能與其他圖資進行套疊整合。現行的幾何改 正方法無論其函數模式，多需要人工量測分佈良好且為數眾多的地面控制點才能獲致理想 成果，此為幾何改正之效率瓶頸。本計畫擬引入三維建物模型作為控制基礎，利用影像處 理技術自動萃取衛星影像上之建物邊緣線，另設計一套半自動化的策略使所有建物模型的 投影能與影像上的建物輪廓線達到最佳套合，以求解最佳之衛星影像幾何改正參數。由於 福衛二號取像時同時記錄時間、衛星軌道位置及姿態，透過此一星曆資料可對影像做系統 改正而得Level 2 影像。本計畫擬透過最小二乘模型-影像套合法（Least-squares Model-image Fitting）計算建物模型與影像最佳套合時之改正參數，以達到精密幾何改正 之Level 3 影像，亦可作為後續正射糾正之基礎。為了驗證本研究所提方法在不同遙測衛 星影像之可行性，擬再採購國外高解析度遙測衛星（如：WorldView-I）之Basic 等級與 Ortho-Ready Standard 等級影像進行實驗。實驗成果將透過3 種方式評估：(1)由數值地形 圖取得地面檢核點座標後展繪於幾何改正後之衛星影像，計算其座標差值；(2)以採購之福 衛二號Level 3 及Level 4 影像作為對照組，比較檢核點之座標差值；(3)以採購國外更高 空間解析度衛星影像，利用本研究所開發之技術進行幾何校正，比較檢核點之座標差值。 本計畫將建立一套半自動化的幾何改正系統，提升福衛二號衛星影像幾何改正之效率，並 驗證於其他高解析度光學遙測衛星影像上之適用性。不僅有助於各子計畫加速處理多時期 衛星影像之效率，並可提升福衛二號衛星影像處理之技術，理論上亦可應用於預定2014 年發射升空之福衛五號影像及其他高解析度光學衛星影像。
Since the successful launch in 2004, the FORMOSAT-II satellite has acquired a great number of remote sensed images of Taiwan. Benefit from its daily re-visit characteristics, the FORMOSAT-II images are very useful for land cover monitoring and change detection, which inspires us for this integrated project proposal. However, the satellite image has to be corrected and registered to the consistent datum coordinate system before it can be interpreted or superimposed onto other maps. Current approaches for geometric correction rely on manually measurements of huge amount of control points, which is the bottleneck of the satellite image processing. The goal of this project is to develop an ad-hoc Least-squares Model-image Fitting (LSMIF) algorithm to semi-automatically determine the orientation parameters of the FORMOSAT-II images. In other words, the images are geometrically corrected by 3D building models instead of control points. Since the operator only has to identify each 3D building model and its approximately position on the image, the efficiency of the manual process can be improved. We will develop a proto-type system to verify the proposed algorithm and the semi-automated strategy. The results will be evaluated by two means. First, the coordinates of the check points on the geometrically corrected image will be compared to their coordinates on the topographic map. Second, the geometrically corrected images will be compared to the level 3 and level 4 images corrected by current approaches. The proposed semi-automated approach not only improves the image processing efficiency of other sub-projects, but also decreases their cost. If the capability of the proposed approach can be proved on the FORMOSAT-II satellite images, it should be also capable for the upcoming FORMOSAT-V satellite images.
Since the successful launch in 2004, the FORMOSAT-II satellite has acquired a great number of remote sensed images of Taiwan. Benefit from its daily re-visit characteristics, the FORMOSAT-II images are very useful for land cover monitoring and change detection, which inspires us for this integrated project proposal. However, the satellite image has to be corrected and registered to the consistent datum coordinate system before it can be interpreted or superimposed onto other maps. Current approaches for geometric correction rely on manually measurements of huge amount of control points, which is the bottleneck of the satellite image processing. The goal of this project is to develop an ad-hoc Least-squares Model-image Fitting (LSMIF) algorithm to semi-automatically determine the orientation parameters of the FORMOSAT-II images. In other words, the images are geometrically corrected by 3D building models instead of control points. Since the operator only has to identify each 3D building model and its approximately position on the image, the efficiency of the manual process can be improved. We will develop a proto-type system to verify the proposed algorithm and the semi-automated strategy. The results will be evaluated by two means. First, the coordinates of the check points on the geometrically corrected image will be compared to their coordinates on the topographic map. Second, the geometrically corrected images will be compared to the level 3 and level 4 images corrected by current approaches. The proposed semi-automated approach not only improves the image processing efficiency of other sub-projects, but also decreases their cost. If the capability of the proposed approach can be proved on the FORMOSAT-II satellite images, it should be also capable for the upcoming FORMOSAT-V satellite images.