CX-6(02)微纳卫星超分辨率成像
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摘要
面向微纳卫星高分辨率对地遥感,将超分辨率成像应用于中国整星60公斤级的CX-6(02)微纳卫星设计中,解决因体积和重量限制导致传统长焦距、大口径成像载荷无法应用于微纳卫星的问题。图像获取上,采用高帧频面阵CMOS探测器对同一地物目标多次曝光的方式,利用卫星姿态控制偏差和地速补偿来获得多帧具有亚像元位移的图像;超分辨率重建算法上,在变分贝叶斯框架下提出加权双向差分模型,提高先验概率模型的方向约束性,削弱观测方程求解的病态性。CX-6(02)星成像数据实验结果表明,本文的图像采样方法可获得较为充分的亚像元信息;相比传统的L1范数先验和全变分先验的变分贝叶斯超分辨算法重建结果,本文重建结果对反卷积运算导致的噪声放大具有更好的抑制作用,可获得两倍分辨率提升,有效提高数据质量和应用价值。
Micro-nano satellite is one of the developing trends of remote sensing technology with the advantages of light-weight, small size,low cost. However, because of the limitation of the volume and weight, the traditional high resolution optical imaging payload with long focal length and large aperture is difficult to apply to earth observation of micro-nano satellite. In order to solve this problem, a super-resolution imaging scheme is demonstrated in this paper. First, in the mode of images acquisition, to obtain multi frame images of the same region, the velocity of imaging relative to the ground should be controlled by satellite attitude. And because the attitude control deviation of satellite is objective, so subpixel displacement information can be generated by pitching, yaw, and rolling random deviations, without to install any other displacement generators; Secondly, In the super-resolution reconstruction algorithm, aiming at solving the problem of prior constraints on variational bayesian super-resolution reconstruction method, we propose a weighted bi-directional difference prior model to overcome the under-constraint of non-edge regions of image due to total variation prior and L1 norm prior, to further restrain the solving space of the observation equation. The above scheme is applied to the China's first super-resolution imaging micro-nano satellite:CX-6(02) micro-nano satellite. The imaging results of this satellite show that: our images acquisition method can obtain sufficient subpixel information, which is approximately uniformly distributed with 0.1 pixel magnitude; The result of super-resolution reconstruction is superior to the same variational bayesian method based on L1 norm prior model and total variation prior model, it is hardly to introduce or amplify the computational noise in the iterative process of the algorithm, effectively weakens the ill-posed property of the deconvolution operation. Make the CX-6(02) satellite's imaging resolution increased from 2.8 to 1.4 meters in the 700 km orbit altitude, and the whole satellite is only 66 kg. Except for micro-nano satellite, the design scheme of this paper can also be applied to medium or large optical imaging satellite, it may provide a certain theoretical and experimental support for high resolution earth-observing remote sensing.
Micro-nano satellite is one of the developing trends of remote sensing technology with the advantages of light-weight, small size,low cost. However, because of the limitation of the volume and weight, the traditional high resolution optical imaging payload with long focal length and large aperture is difficult to apply to earth observation of micro-nano satellite. In order to solve this problem, a super-resolution imaging scheme is demonstrated in this paper. First, in the mode of images acquisition, to obtain multi frame images of the same region, the velocity of imaging relative to the ground should be controlled by satellite attitude. And because the attitude control deviation of satellite is objective, so subpixel displacement information can be generated by pitching, yaw, and rolling random deviations, without to install any other displacement generators; Secondly, In the super-resolution reconstruction algorithm, aiming at solving the problem of prior constraints on variational bayesian super-resolution reconstruction method, we propose a weighted bi-directional difference prior model to overcome the under-constraint of non-edge regions of image due to total variation prior and L1 norm prior, to further restrain the solving space of the observation equation. The above scheme is applied to the China's first super-resolution imaging micro-nano satellite:CX-6(02) micro-nano satellite. The imaging results of this satellite show that: our images acquisition method can obtain sufficient subpixel information, which is approximately uniformly distributed with 0.1 pixel magnitude; The result of super-resolution reconstruction is superior to the same variational bayesian method based on L1 norm prior model and total variation prior model, it is hardly to introduce or amplify the computational noise in the iterative process of the algorithm, effectively weakens the ill-posed property of the deconvolution operation. Make the CX-6(02) satellite's imaging resolution increased from 2.8 to 1.4 meters in the 700 km orbit altitude, and the whole satellite is only 66 kg. Except for micro-nano satellite, the design scheme of this paper can also be applied to medium or large optical imaging satellite, it may provide a certain theoretical and experimental support for high resolution earth-observing remote sensing.
引文
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Villena S,Vega M,Molina R and Katsaggelos A K.2009.Bayesian super-resolution image reconstruction using an?1 prior//Proceedings of the 6th International Symposium on Image and Signal Processing and Analysis.Salzburg,Austria:IEEE:152-157[DOI:10.1109/ISPA.2009.5297740]
Woods M and Katsaggelos A.2017.A Bayesian multi-frame image super-resolution algorithm using the Gaussian Information Filter//Proceedings of 2017 IEEE International Conference on Acoustics,Speech and Signal Processing.New Orleans,LA,USA:IEEE[DOI:10.1109/ICASSP.2017.7952380]
Zhu B,Wang X H,Tang L L and Li C R.2010.Review on methods for snr estimation of optical remote sensing imagery.Remote Sensing Technology and Application,25(2):304-309(朱博,王新鸿,唐伶俐,李传荣.2010.光学遥感图像信噪比评估方法研究进展.遥感技术与应用,25(2):304-309)
Zou M Y.2001.Deconvolution and Signal Recovery.Beijing:National Defense Industry Press(邹谋炎.2001.反卷积和信号复原.北京:国防工业出版社)
Chen C C.2010.A Multi-Frame Super-Resolution Algorithm Using POCS and Wavelet.Montréal,Québec,Canada:Concordia University
Farsiu S,Robinson M D,Elad M and Milanfar P.2004.Fast and robust multiframe super resolution.IEEE Transactions on Image Processing,13(10):1327-1344[DOI:10.1109/TIP.2004.834669]
He Y,Yap K H,Chen L and Chau L P.2007.A nonlinear least square technique for simultaneous image registration and super-resolution.IEEE Transactions on Image Processing,16(11):2830-2841[DOI:10.1109/TIP.2007.908074]
Heinrich G and Goesele M.2009.Variational bayes for generic topic models//Mertsching B,Hund M and Aziz Z,eds.KI 2009:Advances in Artificial Intelligence.Berlin,Heidelberg:Springer,5803:161-168[DOI:10.1007/978-3-642-04617-9_21]
Jacobsen K.2005.High resolution imaging satellite systems//EARSeLWorkshop on Remote Sensing Use of the 3rd Dimension for Remote Sensing Purposes.Porto,Portugal:[s.n.]
Latry C,Vadon H,Lefevre M J and De Boissezon H.2003.SPOT5THX:a 2.5m fused product//Proceedings of the 2nd GRSS/ISPRSJoint Workshop on Remote Sensing and Data Fusion over Urban Areas.Berlin,Germany,Germany:IEEE:87-89.[DOI:10.1109/DFUA.2003.1219963]
Murthy K,Shearn M,Smiley B D,Chau A H,Levine J and Robinson M D.2014.SkySat-1:very high-resolution imagery from a small satellite//Proceedings Volume 9241,Sensors,Systems,and Nextgeneration Satellites XVIII.Amsterdam,Netherlands:SPIE,9241:92411E[DOI:10.1117/12.2074163]
Nitta K,Shogenji R,Miyatake S and Tanida J.2006.Image reconstruction for thin observation module by bound optics by using the iterative backprojection method.Applied Optics,45(13):2893-2900[DOI:10.1364/AO.45.002893]
Shen H F,Li P X and Zhang L P.2005.A regularized super-resolution image reconstruction method.Journal of Image and Graphics,10(4):436-440(沈焕锋,李平湘,张良培.2005.一种基于正则化技术的超分辨影像重建方法.中国图象图形学报,10(4):436-440)[DOI:10.11834/jig.20050489]
Shen H F,Zhang L P,Huang B and Li P X.2007.A MAP approach for joint motion estimation,segmentation,and super resolution.IEEETransactions on Image Processing,16(2):479-490[DOI:10.1109/TIP.2006.888334]
Sun J Y,Tan Z,Lv Q B and Pei L L.2017.Multispectral image enhancement processing for microsat-borne imager//Proceedings of the Volume 10427,Image and Signal Processing for Remote Sensing XXIII.Warsaw,Poland:SPIE:104271W[DOI:10.1117/12.2280556]
Tan Z,Xiang L B,Lv Q B,Sun J Y,Zhao N,Fang Y and Liu Y Y.2017.A sequence images super-resolution enhancement approach based on frequency-domain.Acta Optica Sinica,37(7):0710001(谭政,相里斌,吕群波,孙建颖,赵娜,方煜,刘扬阳.2017.一种基于频域的序列图像超分辨率增强方法.光学学报,37(7):0710001)[DOI:10.3788/aos201737.0710001]
Villena S,Vega M,Molina R and Katsaggelos A K.2009.Bayesian super-resolution image reconstruction using an?1 prior//Proceedings of the 6th International Symposium on Image and Signal Processing and Analysis.Salzburg,Austria:IEEE:152-157[DOI:10.1109/ISPA.2009.5297740]
Woods M and Katsaggelos A.2017.A Bayesian multi-frame image super-resolution algorithm using the Gaussian Information Filter//Proceedings of 2017 IEEE International Conference on Acoustics,Speech and Signal Processing.New Orleans,LA,USA:IEEE[DOI:10.1109/ICASSP.2017.7952380]
Zhu B,Wang X H,Tang L L and Li C R.2010.Review on methods for snr estimation of optical remote sensing imagery.Remote Sensing Technology and Application,25(2):304-309(朱博,王新鸿,唐伶俐,李传荣.2010.光学遥感图像信噪比评估方法研究进展.遥感技术与应用,25(2):304-309)
Zou M Y.2001.Deconvolution and Signal Recovery.Beijing:National Defense Industry Press(邹谋炎.2001.反卷积和信号复原.北京:国防工业出版社)