无人驾驶飞机航空遥感影像匹配及外方位元素解算方法研究
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摘要
无人机作为航空遥感平台发展潜力巨大,已有的卫星遥感和有人机航空遥感技术虽均有获取大面积宏观地理信息的特点,但对于分辨率要求高、更新时间要求快的遥感技术应用却难以保障。将无人机作为航空摄影和对地观测的遥感平台为这种应急需求提供了一种新的技术途径。因此,作为获取高分辨率地面遥感影像的重要方式,无人机航空遥感在摄影测量与遥感应用领域中发挥越来越重要的作用。
正是无人机航空遥感系统具备了诸多卫星遥感与有人机遥感所不具备的优势,所以必将成为遥感应用领域中的重要数据来源,而无人机航空遥感系统虽然有它的优点,但作为一种新型的对地观测平台也带来了新的问题。针对这些问题,本文主要完成了两方面的工作。
1、实现了基于SIFT算子的无人机航空遥感影像的高精度匹配。当无人机低空飞行获取高分辨率遥感影像时,由于不同摄站点拍摄角度不同,使得建筑物等凸出地面的物体在立体像对上成像时产生投影差,导致物体成像几何形状发生畸变并且出现地面高层建筑物之间遮挡现象严重的问题,从而导致匹配的困难,成为影响无人机航空遥感影像匹配质量的主要因素。本文采用对旋转、遮挡、缩放、图像局部灰度变化等都具有较强的稳定性的SIFT算子来进行匹配,并取得了很好的效果。
2、建立了无人机机载姿态数据误差校正模型。无人机飞行时关于姿态信息的所有数据都可以由GPS/INS测得,但是由于多种系统误差和偶然误差的影响,直接将飞行控制数据作为影像外方位元素对所获得的遥感影像进行正射校正处理,将会产生较大误差。本文对无人机机载GPS/INS上获取的姿态信息进行了误差分析,建立了相应的误差检校模型,并利用无人机航摄影像资料验证了该模型的有效性和可行性,从而保证无人机航空遥感影像在无控制点的情况下实现高精度的校正。
The UAV(Unmanned Aerial Vehicle)has great development potentials as the aerial remote sensing platform. Although the existing satellite and manned aerial remote sensing technology both have the characteristics of obtaining large-area macroscopic geographic information, the application of remote sensing technology is difficult to guarantee for the requiremant of higher resolution, faster time to update. A new technology approach is provided by taking UAV as the remote sensing platform of aerial photography and earth observation. Therefore, as the important way of obtaining high-resolution remote sensing images, the UAV remote sensing plays more and more important roles in the field of Photogrammetric and remote sensing applications.
The UAV aerial remote sensing system will inevitably become the important data source of remote sensing application nowsday, because it have many merits that the satellite and manned aerial vehicle don’t have. With the development of the UAV aerial remote sensing system, it also brings new problems as a new earth observation platform. Toward these problems, this paper mainly discuss two aspects of content.
1. We realize high accurate registration of UAV remote sensing images based on SIFT operator in this paper. When the UAV flies at low altitude to obtain high spatial resolution image, the geometric distortions and occlusions phenomena of the stereoscopic images are serious because of central projection imaging and high buildings’projection difference which is caused by different camera’s shooting position. Besides, the buildings which produce projection difference bring the problem of shadow sINSltaneously, which make the gray of the shadow regions changes locally. As a result, it is difficult to match UAV remote sensing images. Therefore, for the low-altitude photography remote sensing platform—UAV, occlusions, distortions and shadow will be the major factors for influencing UAV aerial remote sensing image matching quality. This phenomenon is most obvious in the remote sensing images of cities. By adopting SIFT algorithm, which has strong robustness to rotation, occlusion, scaling, shadow, we would realize the high accurate registration of UAV remote sensing images in this paper. This scheme can obtain a good effect.
2. We establish the systematic error correction model for the UAV airborne GPS/INS in this paper. Unlike the manned aerial vehicle, UAV is a process of automatic control flight, in which the data about the flight position and orientation(the exterior orientation elements) can be measured by the GPS/INS integration on UAV. But because of the influences of many system errors and random errors, if we just ortho-rectify remote sensing images with these flight control data, there will be large errors. The attitude data error analysis and correction method need to be proposed and the corresponding error calibration mode is established in this paper. We calculate out the collimation axis error on the basis of coordinate transformation, and utilize this error to realize the correction of attitude data. The images of the UAV aerial photography is used to verify the effectiveness and feasibility of this model and the experimental results show that the UAV aerial remote sensing images can realize high-precision correction without the ground control points.
正是无人机航空遥感系统具备了诸多卫星遥感与有人机遥感所不具备的优势,所以必将成为遥感应用领域中的重要数据来源,而无人机航空遥感系统虽然有它的优点,但作为一种新型的对地观测平台也带来了新的问题。针对这些问题,本文主要完成了两方面的工作。
1、实现了基于SIFT算子的无人机航空遥感影像的高精度匹配。当无人机低空飞行获取高分辨率遥感影像时,由于不同摄站点拍摄角度不同,使得建筑物等凸出地面的物体在立体像对上成像时产生投影差,导致物体成像几何形状发生畸变并且出现地面高层建筑物之间遮挡现象严重的问题,从而导致匹配的困难,成为影响无人机航空遥感影像匹配质量的主要因素。本文采用对旋转、遮挡、缩放、图像局部灰度变化等都具有较强的稳定性的SIFT算子来进行匹配,并取得了很好的效果。
2、建立了无人机机载姿态数据误差校正模型。无人机飞行时关于姿态信息的所有数据都可以由GPS/INS测得,但是由于多种系统误差和偶然误差的影响,直接将飞行控制数据作为影像外方位元素对所获得的遥感影像进行正射校正处理,将会产生较大误差。本文对无人机机载GPS/INS上获取的姿态信息进行了误差分析,建立了相应的误差检校模型,并利用无人机航摄影像资料验证了该模型的有效性和可行性,从而保证无人机航空遥感影像在无控制点的情况下实现高精度的校正。
The UAV(Unmanned Aerial Vehicle)has great development potentials as the aerial remote sensing platform. Although the existing satellite and manned aerial remote sensing technology both have the characteristics of obtaining large-area macroscopic geographic information, the application of remote sensing technology is difficult to guarantee for the requiremant of higher resolution, faster time to update. A new technology approach is provided by taking UAV as the remote sensing platform of aerial photography and earth observation. Therefore, as the important way of obtaining high-resolution remote sensing images, the UAV remote sensing plays more and more important roles in the field of Photogrammetric and remote sensing applications.
The UAV aerial remote sensing system will inevitably become the important data source of remote sensing application nowsday, because it have many merits that the satellite and manned aerial vehicle don’t have. With the development of the UAV aerial remote sensing system, it also brings new problems as a new earth observation platform. Toward these problems, this paper mainly discuss two aspects of content.
1. We realize high accurate registration of UAV remote sensing images based on SIFT operator in this paper. When the UAV flies at low altitude to obtain high spatial resolution image, the geometric distortions and occlusions phenomena of the stereoscopic images are serious because of central projection imaging and high buildings’projection difference which is caused by different camera’s shooting position. Besides, the buildings which produce projection difference bring the problem of shadow sINSltaneously, which make the gray of the shadow regions changes locally. As a result, it is difficult to match UAV remote sensing images. Therefore, for the low-altitude photography remote sensing platform—UAV, occlusions, distortions and shadow will be the major factors for influencing UAV aerial remote sensing image matching quality. This phenomenon is most obvious in the remote sensing images of cities. By adopting SIFT algorithm, which has strong robustness to rotation, occlusion, scaling, shadow, we would realize the high accurate registration of UAV remote sensing images in this paper. This scheme can obtain a good effect.
2. We establish the systematic error correction model for the UAV airborne GPS/INS in this paper. Unlike the manned aerial vehicle, UAV is a process of automatic control flight, in which the data about the flight position and orientation(the exterior orientation elements) can be measured by the GPS/INS integration on UAV. But because of the influences of many system errors and random errors, if we just ortho-rectify remote sensing images with these flight control data, there will be large errors. The attitude data error analysis and correction method need to be proposed and the corresponding error calibration mode is established in this paper. We calculate out the collimation axis error on the basis of coordinate transformation, and utilize this error to realize the correction of attitude data. The images of the UAV aerial photography is used to verify the effectiveness and feasibility of this model and the experimental results show that the UAV aerial remote sensing images can realize high-precision correction without the ground control points.
引文
[1]何定洲.无人机遥感空中控制系统硬件研制及地面下传数据通道的实验研究[D].南京航空航天大学硕士论文,2006.
[2]郭华东.对地观测与可持续发展[M].科学出版社,2001.
[3]马瑞升,孙涵,林宗桂,马轮基,吴朝晖,黄耀.微型无人机遥感影像的纠偏与定位[J].南京气象学院学报,2005,28(5):632- 639.
[4]袁修孝,宋妍.一种运用纹理和光谱特征消除投影差影响的建筑物变化检测方法[J].武汉大学学报(信息科学版),2007.
[5]徐丽华.顾及投影差的遥感影像变化检测[D].武汉大学,2005.
[6] B?umker, M. New Calibration and Computing Method for Direct Georeferencing of Image and Scanner Data Using the Position and Angular Data of An Hybrid Inertial Navigation System.Integrated Sensor Orientation,43,197-212,2002.
[7] J.Skaloud,P.Schaer.Towards A More Rigorous Boresight Calibration.ISPRS International Workshop on Theory,Technical and Realities of Inerial/GPS Sensor Oriantation,Commission 1, WGI/5, Castelldefels, Spain, 22-23,2003.
[8]宁津生,陈俊勇,李德仁,刘经南,张祖勋.测绘学概论[M].武汉大学出版社,2004.
[9]董绪荣,张守信,华仲春.GPS/INS组合导航定位及其应用[M].国防科技大学出版社,1998.
[10]袁修孝.GPS辅助空中三角测量原理及应用[M].测绘出版社,2001.
[11]李学友.INS/DGPS辅助航空摄影测量原理、方法及实践[D].解放军信息工程大学测绘学院,2005.
[12]刘志俭.GPS载波相位差分技术、捷联惯性导航系统初始对准技术及其组合技术研究[D].长沙国防科学技术大学,2003.
[13]赵永德.载波相位测量技术在GPS/INS中应用的研究[D].哈尔滨工业大学,2003.
[14]张红梅,赵建虎.水库库容和淤积测量技术研究[J].水利学报,2002,(12):33-37.
[15]刘经南,陈俊勇,张燕平,李毓麟,葛茂荣.广域差分GPS原理和方法[M].测绘出版社,1998.
[16]张宗麟.惯性导航与组合导航[M].航空工业出版社,2000:2.
[17]宋瑞子.GPS/SINS组合导航系统中误差及精度研究[D].哈尔滨工程大学,2002.
[18]袁建华,殷学民,邹谋炎.一种用于图像超分辨的实时高精度像素内配准方法[D].中国科学院电子学研究所,2007.
[19]葛永新,杨丹,张小洪.基于特征点对齐度的图像配准方法[D].重庆大学数理学院,2007.
[20] Krystian Mikolajczyk,Cordelia Schmid.A Performance Evaluation of Local Descriptors[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol 27,10, 2005:1615-1630.
[21]单欣,王耀明,董建萍.基于RANSAC算法的基本矩阵估计的匹配方法.上海电机学院学报,2006.
[22]孙家抦.遥感原理与应用[M].武汉大学出版社,2003:2.
[23] David G,Lowe.Distinctive Image Features From Scale-invariant KeyPoints[J].Intemational Joumal of Computer Vision,60, 2(2004): 91-110.
[24] Yan Ke,Rahul Sukthankar.PCA-SIFT:A More Distinctive RePresentation for Local Image Descriptors[J].2004.
[25] Besl P,Jain R.Segmentation and Classification of Range Images[J].IEEE Transaetions on Pattern Analysis and Machine Intelligence,1987,9:608-620.
[26] I. Stamos,P.K.Allen.3-D Model Construction Using Range and Image Data[J].Computer Vision and Pattern Recognition,2000.
[27] Besl P J,McKay N D.A Method for Registration of 3-D Shapes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(2): 239-256.
[28] Y.Yu,A.Ferencz,J.Malik.Extracting Objects from Range and Radiance Images[J]. IEEE Trans, Visualization and Computer Graphics,vol.7,2001.
[29] Lowe D.G.Distinctive Image Features from Scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[30] Faugeras O,Robert L.What Can Two Images Tell Us About the Third One[J].Proceedings of the Europe Conference on Computer Vision,Sweden,1994.
[31] Koenderink J.The Structure of images[J].Biological Cybernetics,1984,50: 363-396.
[32] Lindeberg T.Scale-Space for Discrete Signals[J].IEEE Transactions PAMI,1980,207:187-217.
[33] Babaud J,Witkin A P,Baudin Metal Uniqueness of the Gaussian kernel for scale-space filtering[J].IEEE Transactions on Pattem Analysis and Machine Intelligence,1996,8(1):26-33.
[34]郭大海,吴立新,王建超.机载POS系统对地定位方法初探[J].国土资源遥感,2004,60 (2):26-31.
[35] Applanix.Computation of Phi Omega Kappa from Roll Pitch and Heading[OL].http://www.applanix.com,2000.
[36] Cramer, M,Stallman D.System Calibration for Direct Georeferencing[J].International Archives of Photogrammetry and Remote Sensing,2002,34:79-84.
[37]袁修孝.GPS辅助空中三角测量原理及应用[M].测绘出版社,2001.
[38]龚真春.GPS在微型无人机导航定位中的研究与应用[D].浙江大学,2005.
[39] Skaloud, J.Rigorous Approach to Boresight Self-calibration in Airborne Laser Scanning[J].ISPRS Journal of Photogrammetry & Remote Sensing,2006,61,47-59.
[40] Mostafa, M.Camera/INS Boresight Calibration:New Advances and Performance Analysis[J]. ASPRS Meeting,Washington,2002.
[2]郭华东.对地观测与可持续发展[M].科学出版社,2001.
[3]马瑞升,孙涵,林宗桂,马轮基,吴朝晖,黄耀.微型无人机遥感影像的纠偏与定位[J].南京气象学院学报,2005,28(5):632- 639.
[4]袁修孝,宋妍.一种运用纹理和光谱特征消除投影差影响的建筑物变化检测方法[J].武汉大学学报(信息科学版),2007.
[5]徐丽华.顾及投影差的遥感影像变化检测[D].武汉大学,2005.
[6] B?umker, M. New Calibration and Computing Method for Direct Georeferencing of Image and Scanner Data Using the Position and Angular Data of An Hybrid Inertial Navigation System.Integrated Sensor Orientation,43,197-212,2002.
[7] J.Skaloud,P.Schaer.Towards A More Rigorous Boresight Calibration.ISPRS International Workshop on Theory,Technical and Realities of Inerial/GPS Sensor Oriantation,Commission 1, WGI/5, Castelldefels, Spain, 22-23,2003.
[8]宁津生,陈俊勇,李德仁,刘经南,张祖勋.测绘学概论[M].武汉大学出版社,2004.
[9]董绪荣,张守信,华仲春.GPS/INS组合导航定位及其应用[M].国防科技大学出版社,1998.
[10]袁修孝.GPS辅助空中三角测量原理及应用[M].测绘出版社,2001.
[11]李学友.INS/DGPS辅助航空摄影测量原理、方法及实践[D].解放军信息工程大学测绘学院,2005.
[12]刘志俭.GPS载波相位差分技术、捷联惯性导航系统初始对准技术及其组合技术研究[D].长沙国防科学技术大学,2003.
[13]赵永德.载波相位测量技术在GPS/INS中应用的研究[D].哈尔滨工业大学,2003.
[14]张红梅,赵建虎.水库库容和淤积测量技术研究[J].水利学报,2002,(12):33-37.
[15]刘经南,陈俊勇,张燕平,李毓麟,葛茂荣.广域差分GPS原理和方法[M].测绘出版社,1998.
[16]张宗麟.惯性导航与组合导航[M].航空工业出版社,2000:2.
[17]宋瑞子.GPS/SINS组合导航系统中误差及精度研究[D].哈尔滨工程大学,2002.
[18]袁建华,殷学民,邹谋炎.一种用于图像超分辨的实时高精度像素内配准方法[D].中国科学院电子学研究所,2007.
[19]葛永新,杨丹,张小洪.基于特征点对齐度的图像配准方法[D].重庆大学数理学院,2007.
[20] Krystian Mikolajczyk,Cordelia Schmid.A Performance Evaluation of Local Descriptors[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,Vol 27,10, 2005:1615-1630.
[21]单欣,王耀明,董建萍.基于RANSAC算法的基本矩阵估计的匹配方法.上海电机学院学报,2006.
[22]孙家抦.遥感原理与应用[M].武汉大学出版社,2003:2.
[23] David G,Lowe.Distinctive Image Features From Scale-invariant KeyPoints[J].Intemational Joumal of Computer Vision,60, 2(2004): 91-110.
[24] Yan Ke,Rahul Sukthankar.PCA-SIFT:A More Distinctive RePresentation for Local Image Descriptors[J].2004.
[25] Besl P,Jain R.Segmentation and Classification of Range Images[J].IEEE Transaetions on Pattern Analysis and Machine Intelligence,1987,9:608-620.
[26] I. Stamos,P.K.Allen.3-D Model Construction Using Range and Image Data[J].Computer Vision and Pattern Recognition,2000.
[27] Besl P J,McKay N D.A Method for Registration of 3-D Shapes[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1992,14(2): 239-256.
[28] Y.Yu,A.Ferencz,J.Malik.Extracting Objects from Range and Radiance Images[J]. IEEE Trans, Visualization and Computer Graphics,vol.7,2001.
[29] Lowe D.G.Distinctive Image Features from Scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[30] Faugeras O,Robert L.What Can Two Images Tell Us About the Third One[J].Proceedings of the Europe Conference on Computer Vision,Sweden,1994.
[31] Koenderink J.The Structure of images[J].Biological Cybernetics,1984,50: 363-396.
[32] Lindeberg T.Scale-Space for Discrete Signals[J].IEEE Transactions PAMI,1980,207:187-217.
[33] Babaud J,Witkin A P,Baudin Metal Uniqueness of the Gaussian kernel for scale-space filtering[J].IEEE Transactions on Pattem Analysis and Machine Intelligence,1996,8(1):26-33.
[34]郭大海,吴立新,王建超.机载POS系统对地定位方法初探[J].国土资源遥感,2004,60 (2):26-31.
[35] Applanix.Computation of Phi Omega Kappa from Roll Pitch and Heading[OL].http://www.applanix.com,2000.
[36] Cramer, M,Stallman D.System Calibration for Direct Georeferencing[J].International Archives of Photogrammetry and Remote Sensing,2002,34:79-84.
[37]袁修孝.GPS辅助空中三角测量原理及应用[M].测绘出版社,2001.
[38]龚真春.GPS在微型无人机导航定位中的研究与应用[D].浙江大学,2005.
[39] Skaloud, J.Rigorous Approach to Boresight Self-calibration in Airborne Laser Scanning[J].ISPRS Journal of Photogrammetry & Remote Sensing,2006,61,47-59.
[40] Mostafa, M.Camera/INS Boresight Calibration:New Advances and Performance Analysis[J]. ASPRS Meeting,Washington,2002.