系統識別號 U0026-1508201914491900
論文名稱(中文) 微衛星推幅式影像自動化對位
論文名稱(英文) Automatic Registration for Microsatellite Push-Frame Images
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 107
學期 2
出版年 108
研究生(中文) 張雅筑
研究生(英文) Ya-Chu Chang
學號 P66064031
學位類別 碩士
語文別 英文
論文頁數 75頁
口試委員 指導教授-曾義星
中文關鍵字 微衛星推幅式影像  相對方位  外方位 
英文關鍵字 Microsatellite Push-Frame Images  Relative Orientation  Exterior Orientation 
中文摘要 近年來,商用地球觀測衛星在體積與重量上有微小化的趨勢,將衛星微小化的優點在於可大幅降低發射的成本,使人們能以相對低的成本在太空中佈署大量的衛星,因而提高衛星觀測的時間解析度。SkySat-1微衛星是其中一個成功的例子,除了上述的特點,SkySat-1 與傳統推帚式光學衛星不同的特點是採用了推幅式面型感測器,單次曝光即獲得像幅式影像,短時間內連續拍攝的影像能夠具有高重疊率。衛星影像通常會先經過輻射與幾何校正才被實際應用,在幾何校正方面,傳統上多以嚴密幾何模式(rigorous sensor models)或有理函數模式(Rational Function Models, RFM)建立物像轉換關係。然而針對高重疊像幅式影像處理,電腦視覺領域發展出運動回復結構 (Structure from Motion, SfM) 的方法建立物像關係。雖然運動回復結構一般被用於近景攝影測量,且目的多著重於重建三維場景的模型或坐標,但基於微衛星面型感測器的取像模式,本研究嘗試以基於SfM的方式,自動化求解微衛星推幅式影像之匹配及對位。
本研究主要可分為兩階段,分別是對於一組影像對的相對方位求解以及一條航帶上連續影像的外方位估計。在一條航帶中,任兩張具有一定重疊比例的影像,其相對方位關係可以透過影像特徵的對應關係來求得。而獲取可靠且穩定的相對方位必須仰賴精準、數量足夠且分布均勻的影像特徵。因此,本研究的第一部分使用影像特徵匹配策略,並搭配合適的除錯方法得到準確的特徵對應關係。利用特徵對應關係可以計算本質矩陣(Essential Matrix)並進一步分解出相對方位參數。獲得兩兩影像間的相對方位後,可以透過這些相對方位重建所有影像的外方位。本研究提出以相對方位網型平差估計外方位參數的方式,並將其分為漸進式解法與單一步驟解法。漸進式解法分為兩階段,第一階段先求算旋轉參數,第二階段將旋轉參數視為已知值並求算位置參數及真實尺度。單一步驟解法則是同時求解旋轉參數、位置參數及真實尺度。在平差模型中,除了相對方位觀測量外,還加入利用微衛星載體資料估算得的影像基線尺度作為觀測量。這對於解決求解位置參數時的退化問題(即相機以線性的方式移動)是必要的。
為了證明以基於SfM的方式自動化求解微衛星航帶影像外方外參數的可行性,本研究以高空航拍的方式模擬微衛星面型感測器的取像,取其中連續的10張影像進行相對方位與外方位的求解,並將成果與以空中三角測量(Aerial Triangulation, AT)解算得的外方位參數進行比較。實驗結果顯示漸進式解法與單一步驟解法皆可以計算出合理的影像外方位,而漸進式解法在位置參數的計算成果略優於單一步驟解法。
英文摘要 In recent years, commercial Earth observation satellites have become smaller in size and weight. The advantage of miniaturizing satellites is that the cost of launching can be significantly reduced, allowing people to operate large numbers of satellites in space at relatively low cost and thereby increasing the time resolution of satellite observations. One of the successful examples is the SkySat-1 microsatellite. In addition to the above advantages, the SkySat-1 differs from conventional satellites in that it uses a push-frame sensor. Therefore, in the same flight strip, images continuously captured in a short time can have a high overlap. Satellite imagery is usually applied after radiometric and geometric corrections. In terms of geometric correction, the conversion between object space and image space is traditionally established by using rigorous sensor models or Rational Function Models (RFMs). On the other hand, the method of establishing the relationship between object space and image space has also developed in the field of computer vision. A well-known method is Structure from Motion (SfM). Although SfM is generally used for close-range photogrammetry, and the purpose is more focused on reconstructing the model or coordinates of the 3D scene, based on the imaging mode of the microsatellite push-frame sensor, this study aims at exploring the feasibility of automatic registration of microsatellite push-frame images based on the SfM-based approach.
The research approach can be divided into two stages, which are the ROP estimation of a single image pair and the global EOP estimation of a set of images. In a flight strip, the ROPs of any two sufficiently overlapped images can be obtained through the correspondences of image features. Obtaining reliable and stable ROPs must rely on accurate, adequately numbered and evenly distributed image features. Therefore, the first part of this study uses image feature matching strategy, and with appropriate error matching filtering methods to get accurate feature correspondences. The feature correspondences can be used to calculate the essential matrix and further decompose it into ROPs. The EOPs of all involved images can then be reconstructed through these ROPs. An incremental approach and a one-step adjustment to estimate the EOPs by ROP network adjustment are proposed. The incremental approach is divided into two steps. In the first step, the rotation parameters are calculated. In the second step, the rotation parameters are regarded as known values and the unknown position parameters and the true scales are solved. By contrast, the one-step adjustment solves the rotation parameters, position parameters, and true scales simultaneously. In the least-squares adjustment model, in addition to the ROP observations, the baseline scales of the images estimated by using the microsatellite on-board data are also used as observations. This is necessary to resolve the degeneracy in translation estimation.
In order to prove the feasibility of automatically registering microsatellite push-frame images by SfM-based methods, this study simulates the microsatellite push-frame images by high altitude aerial photography and takes 10 consecutive images for ROP and EOP estimation. The results are compared with the EOPs solved by aerial triangulation (AT). Experimental results show that both the incremental approach and the one-step adjustment can provide reasonable EOPs of the images, while the accuracy of the incremental approach is slightly better than the one-step solution in the calculation of translation parameters.
論文目次 摘要 I
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Objective 6
1.3 Research Approach 7
1.4 Thesis Structure 9
Chapter 2 Image Matching and Relative Orientation 10
2.1 Feature Detection, Extraction and Matching 10
2.2 Random Sample Consensus (RANSAC) 14
2.3 Relative Orientation Recovery 28
2.4 Epipolar Line 33
2.5 Recovering ROPs from the Essential Matrix 35
Chapter 3 ROP Network Adjustment 38
3.1 ROPs of Image Pairs 38
3.2 Consistent ROPs and EOPs 40
3.3 ROP Network Adjustment 42
3.3.1 Incremental Approach 43
3.3.2 One-step Adjustment 46
Chapter 4 Experiments 47
4.1 Test Data 47
4.2 ROP Estimation and Network Adjustment 51
4.2.1 ROP Estimation 51
4.2.1 ROP Network Adjustment 55
Chapter 5 Conclusions and Suggestions 62
5.1 Conclusions 62
5.2 Suggestions 64

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