多体制遥感卫星成像数据高精度处理新方法
A new method for high precision processing of multi-system Earth observation satellite data
- 2023年27卷第7期 页码：1511-1522
收稿：2023-05-31，
纸质出版：2023-07-07
DOI： 10.11834/jrs.20233181
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摘要
光学和SAR等对地观测卫星需要经过成像、辐射/几何校正等处理和不断的时序积累，才能为计算机解译提供精度高、稳定性好、时间持续的数据和特征。传统中低分辨率对地观测卫星通常基于地理网格内地物目标电磁波散射特性简化为理想点目标的假设，进行逐像素处理。然而，高分宽幅、大斜视、多通道等新体制卫星的工作模式更加复杂，其数据处理对星地全链路各环节产生的误差非常敏感，对成像参数标定或估计的精度提出了更高要求，此时基于理想点目标假设来进行参数估计、成像及校正处理的方式已难以满足处理精度要求。并且，近年来多体制卫星组网式协同和融合应用的新发展，也使得当前的理想点目标假设难以表征和建模多源多时相数据特征。为此，本文提出了多体制遥感卫星成像数据高精度处理的新方法，首先创新提出了“超像素”的概念和表征理论框架，建立了基于超像素的精确成像模型，然后通过挖掘超像素稳定特征并借鉴生成对抗学习机制，实现了星地全链路高耦合成像参数的高精度估计和持续精化，有效提升了多体制遥感卫星成像数据产品的精度，为计算机解译提供了好的数据产品输入。
Abstract
Earth observation satellites
such as optical and SAR satellites
require processing such as imaging
radiometric/geometric correction
and continuous accumulation in order to provide high-precision
stable
and time continuous data and features for computer interpretation. Traditional medium and low resolution Earth observation satellites typically perform pixel-by-pixel processing based on the assumption of ideal point targets
which means that the ground object grid has a invariant time-frequency characteristic. However
the working modes of advanced satellite systems
such as high-resolution
wide-swath
large squint angle
and multi-channel
are more complex
and their data processing is very sensitive to the errors generated in the whole chain of the satellite to ground
which puts higher requirements on the accuracy of imaging parameter calibration or estimation. Hence
the method of assuming sensor pixels as ideal point targets for parameter estimation
imaging
and correction processing is no longer able to meet the processing accuracy requirements. Moreover
in recent years
the new development of multi-system satellite network collaboration and fusion applications has made it difficult to characterize and model the features of multi-source and multi-temporal data based on the current ideal point target assumption. To this end
this article proposes a new method for high-precision processing of multi-system remote sensing satellite imaging data. Firstly
the concept and characterization theory of “Hyper-pixel” are proposed
and an accurate imaging model based on hyper-pixels is established. Then
by mining stable features of hyper-pixels
and inspired by generative adversarial learning mechanisms
high-precision estimation and continuous refinement of high coupling imaging parameters are achieved. This effectively improves the accuracy of multi-system remote sensing satellite imaging data products
and provides better data input for computer interpretation.
关键词
Keywords
references
Cui L , Qiu X L , Guo J Y , Wen X J , Yang J Y and Fu K . 2020 . A multi-channel phase error estimation method based on an error backpropagation algorithm for a multichannel SAR . Journal of Radars , 9 ( 5 ): 878 - 885
崔磊 , 仇晓兰 , 郭嘉逸 , 温雪娇 , 杨俊莹 , 付琨 . 2020 . 一种基于误差反向传播优化的多通道SAR相位误差估计方法 . 雷达学报 , 9 ( 5 ): 878 - 885 [ DOI: 10.12000/JR20096 http://dx.doi.org/10.12000/JR20096 ]
Deng Y K , Yu W D , Zhang H , Wang W , Liu D C and Wang Y . 2020 . Forthcoming spaceborne SAR development . Journal of Radars , 9 ( 1 ): 1 - 33
邓云凯 , 禹卫东 , 张衡 , 王伟 , 刘大成 , 王宇 . 2020 . 未来星载SAR技术发展趋势 . 雷达学报 , 9 ( 1 ): 1 - 33 [ DOI: 10.12000/JR20008 http://dx.doi.org/10.12000/JR20008 ]
Deng Y K , Zhao F J and Wang Y . 2012 . Brief analysis on the development and application of spaceborne SAR . Journal of Radars , 1 ( 1 ): 1 - 10
邓云凯 , 赵凤军 , 王宇 . 2012 . 星载SAR技术的发展趋势及应用浅析 . 雷达学报 , 1 ( 1 ): 1 - 10 [ DOI: 10.3724/SP.J.1300.2012.20015 http://dx.doi.org/10.3724/SP.J.1300.2012.20015 ]
Freeman A . 1992 . SAR calibration: an overview . IEEE Transactions on Geoscience and Remote Sensing , 30 ( 6 ): 1107 - 1121 [ DOI: 10.1109/36.193786 http://dx.doi.org/10.1109/36.193786 ]
Fu K , Sun X , Qiu X L , Diao W H , Yan Z Y , Huang L J and Yu H F . 2021 . Multi-satellite integrated processing and analysis method under remote sensing big data . Journal of Remote Sensing , 25 ( 3 ): 691 - 707
付琨 , 孙显 , 仇晓兰 , 刁文辉 , 闫志远 , 黄丽佳 , 于泓峰 . 2021 . 遥感大数据条件下多星一体化处理与分析 . 遥感学报 , 25 ( 3 ): 691 - 707 [ DOI: 10.11834/JRS.20211058 http://dx.doi.org/10.11834/JRS.20211058 ]
Fu K , You H J and Hu Y F . 2007 . Fine registration technology of multi-temporal spaceborne SAR image . Remote Sensing Technology and Application , 22 ( 5 ): 637 - 641
付琨 , 尤红建 , 胡岩峰 . 2007 . 多时相星载SAR图像精配准技术研究 . 遥感技术与应用 , 22 ( 5 ): 637 - 641 [ DOI: 10.3969/j.issn.1004-0323.2007.05.012 http://dx.doi.org/10.3969/j.issn.1004-0323.2007.05.012 ]
Fu X Y , You H J and Fu K . 2012 . A statistical approach to detect edges in SAR images based on square successive difference of averages . IEEE Geoscience and Remote Sensing Letters , 9 ( 6 ): 1094 - 1098 [ DOI: 10.1109/LGRS.2012.2190378 http://dx.doi.org/10.1109/LGRS.2012.2190378 ]
Fu X Y , You H J and Fu K . 2012 . Building segmentation from high-resolution sar images based on improved markov random field . Acta Electronica Sinica , 40 ( 6 ): 1141 - 1147
傅兴玉 , 尤红建 , 付琨 . 2012 . 基于改进Markov随机场的高分辨率SAR图像建筑物分割算法 . 电子学报 , 40 ( 6 ): 1141 - 1147 [ DOI: 10.3969/j.issn.0372-2112.2012.06.012 http://dx.doi.org/10.3969/j.issn.0372-2112.2012.06.012 ]
Han B , Zhang Y J , Hu D H and Huang L J . 2011 . Research on mending of space-borne sliding spotlight SAR imaging model error . Journal of Electronics and Information Technology , 33 ( 7 ): 1694 - 1699
韩冰 , 张永军 , 胡东辉 , 黄丽佳 . 2011 . 星载滑动聚束SAR成像模型误差校正方法研究 . 电子与信息学报 , 33 ( 7 ): 1694 - 1699 [ DOI: 10.3724/SP.J.1146.2010.01259 http://dx.doi.org/10.3724/SP.J.1146.2010.01259 ]
Jiao N G , Wang F and You H J . 2020a . A new RD-RFM stereo geolocation model for 3D geo-information reconstruction of SAR-optical satellite image pairs . IEEE Access , 8 : 94654 - 94664 [ DOI: 10.1109/ACCESS.2020.2991199 http://dx.doi.org/10.1109/ACCESS.2020.2991199 ]
Jiao N G , Wang F , You J , Liu J Y and Qiu X L . 2020b . A generic framework for improving the geopositioning accuracy of multi-source optical and SAR imagery . ISPRS Journal of Photogrammetry and Remote Sensing , 169 : 377 - 388 [ DOI: 10.1016/j.isprsjprs.2020.09.017 http://dx.doi.org/10.1016/j.isprsjprs.2020.09.017 ]
Li D R and Wang M . 2020 . A review of high resolution optical satellite surveying and mapping technology . Spacecraft Recovery and Remote Sensing , 41 ( 2 ): 1 - 11
李德仁 , 王密 . 2020 . 高分辨率光学卫星测绘技术综述 . 航天返回与遥感 , 41 ( 2 ): 1 - 11 [ DOI: 10.3969/j.issn.1009-8518.2020.02.001 http://dx.doi.org/10.3969/j.issn.1009-8518.2020.02.001 ]
Li D R , Wang M and Jiang J . 2021 . China’s high resolution optical remote sensing satellites and their mapping applications . Geo-Spatial Information Science , 24 ( 1 ): 85 - 94 [ DOI: 10.1080/10095020.2020.1838957 http://dx.doi.org/10.1080/10095020.2020.1838957 ]
Li J D . 2022 . Advances in high-resolution earth observation satellite remote sensing technologies in China . Science and Technology Foresight , 1 ( 1 ): 112 - 125
李劲东 . 2022 . 中国高分辨率对地观测卫星遥感技术进展 . 前瞻科技 , 1 ( 1 ): 112 - 125 [ DOI: 10.3981/j.issn.2097-0781.2022.01.010 http://dx.doi.org/10.3981/j.issn.2097-0781.2022.01.010 ]
Li X W and Wang Y T . 2013 . Prospects on future developments of quantitative remote sensing . Acta Geographica Sinica , 68 ( 9 ): 1163 - 1169
李小文 , 王祎婷 . 2013 . 定量遥感尺度效应刍议 . 地理学报 , 68 ( 9 ): 1163 - 1169 [ DOI: 10.11821/flxb201309001 http://dx.doi.org/10.11821/flxb201309001 ]
Li Z Y , Guo J Y , Zhang Y T , Huang L J , Li J and Wu Y R . 2022 . A novel autofocus algorithm for ship targets in SAR images based on the adaptive momentum estimation optimizer and space-variant minimum entropy criteria . Journal of Radars , 11 ( 1 ): 83 - 94
李志远 , 郭嘉逸 , 张月婷 , 黄丽佳 , 李洁 , 吴一戎 . 2022 . 基于自适应动量估计优化器与空变最小熵准则的SAR图像船舶目标自聚焦算法 . 雷达学报 , 11 ( 1 ): 83 - 94 [ DOI: 10.12000/JR21159 http://dx.doi.org/10.12000/JR21159 ]
Liu A P , Liu Z , Fu K and You H J . 2009 . High resolution SAR images multi-layer segmentation based on graph partitioning [C]// 2009 International Conference on Information Engineering and Computer Science . Wuhan : IEEE: 1 - 4 [ DOI: 10.1109/ICIECS.2009.5365016 http://dx.doi.org/10.1109/ICIECS.2009.5365016 ]
Luo Y T , Qiu X L , Dong Q and Fu K . 2021 . A robust stereo positioning solution for multiview spaceborne SAR images based on the range–Doppler model . IEEE Geoscience and Remote Sensing Letters , 19 : 4008705 [ DOI: 10.1109/LGRS.2020.3048731 http://dx.doi.org/10.1109/LGRS.2020.3048731 ]
Niu S R , Qiu X L , Lei B , Ding C B and Fu K . 2020 . Parameter extraction based on deep neural network for SAR target simulation . IEEE Transactions on Geoscience and Remote Sensing , 58 ( 7 ): 4901 - 4914 [ DOI: 10.1109/TGRS.2020.2968493 http://dx.doi.org/10.1109/TGRS.2020.2968493 ]
Niu S R , Qiu X L , Lei B and Fu K . 2021 . A SAR target image simulation method with DNN embedded to calculate electromagnetic reflection . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 14 : 2593 - 2610 [ DOI: 10.1109/JSTARS.2021.3056920 http://dx.doi.org/10.1109/JSTARS.2021.3056920 ]
Shangguan S T , Qiu X L and Fu K . 2023 . Research on a special hyper-pixel for SAR radiometric monitoring . Remote Sensing , 15 ( 8 ): 2175 [ DOI: 10.3390/rs15082175 http://dx.doi.org/10.3390/rs15082175 ]
Shangguan S T , Qiu X L , Fu K , Lei B and Hong W . 2020 . GF-3 polarimetric data quality assessment based on automatic extraction of distributed targets . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 13 : 4282 - 4294 [ DOI: 10.1109/JSTARS.2020.3012151 http://dx.doi.org/10.1109/JSTARS.2020.3012151 ]
Shangguan S T , Qiu X L , Han B , Liu W J and Fu K . 2022 . A novel polarimetric channel imbalance phase estimation method based on the rotated double-bounce backscatters in urban areas . Remote Sensing , 14 ( 13 ): 3177 [ DOI: 10.3390/rs14133177 http://dx.doi.org/10.3390/rs14133177 ]
Shangguan S T , Qiu X L , Yang J T , Lei B and Din C B . 2019 . Extraction and analysis of the scattering stability in urban areas based on dual-polarization SAR data . IEEE Geoscience and Remote Sensing Letters , 16 ( 3 ): 427 - 431 [ DOI: 10.1109/LGRS.2018.2873624 http://dx.doi.org/10.1109/LGRS.2018.2873624 ]
Sun X , Tian Y , Lu W X , Wang P J , Niu R G , Yu H F and Fu K . 2023 . From single- to multi-modal remote sensing imagery interpretation: a survey and taxonomy . Science China Information Sciences , 66 ( 4 ): 140301 [ DOI: 10.1007/s11432-022-3588-0 http://dx.doi.org/10.1007/s11432-022-3588-0 ]
Sun X , Wang P J , Lu W X , Zhu Z C , Lu X N , He Q B , Li J X , Rong X E , Yang Z J , Chang H , He Q L , Yang G , Wang R P , Lu J W and Fu K . 2022 . RingMo: a remote sensing foundation model with masked image modeling . IEEE Transactions on Geoscience and Remote Sensing , 61 : 5612822 [ DOI: 10.1109/TGRS.2022.3194732 http://dx.doi.org/10.1109/TGRS.2022.3194732 ]
Wang S H , You H J and Fu K . 2012 . BFSIFT: a novel method to find feature matches for SAR image registration . IEEE Geoscience and Remote Sensing Letters , 9 ( 4 ): 649 - 653 [ DOI: 10.1109/LGRS.2011.2177437 http://dx.doi.org/10.1109/LGRS.2011.2177437 ]
Wu Y R . 2013 . Concept on multidimensional space joint-observation SAR . Journal of Radars , 2 ( 2 ): 135 - 142
吴一戎 . 2013 . 多维度合成孔径雷达成像概念 . 雷达学报 , 2 ( 2 ): 135 - 142 [ DOI: 10.3724/SP.J.1300.2013.13047 http://dx.doi.org/10.3724/SP.J.1300.2013.13047 ]
Xiang Y M , Wang F , and You H J . 2018 . OS-SIFT: a robust SIFT-like algorithm for high-resolution optical-to-SAR image registration in suburban areas . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 6 ): 3078 - 3090 [ DOI: 10.1109/TGRS.2018.2790483 http://dx.doi.org/10.1109/TGRS.2018.2790483 ]
Xu K F , Lei B and Zhang Y T . 2018 . Retrieval of land surface emissivity using spectral and texture features based on neural network . Journal of University of Chinese Academy of Sciences , 35 ( 1 ): 102 - 108
徐开发 , 雷斌 , 张月婷 . 2018 . 基于光谱纹理特征融合和神经网络的地表发射率获取方法 . 中国科学院大学学报 , 35 ( 1 ): 102 - 108 [ DOI: 10.7523/j.issn.2095-6134.2018.01.014 http://dx.doi.org/10.7523/j.issn.2095-6134.2018.01.014 ]
Xu Z Q , Zhang B C , Zhang Z , Wang M Z and Wu Y R . 2022 . Nonconvex-nonlocal total variation regularization-based joint feature-enhanced sparse SAR imaging . IEEE Geoscience and Remote Sensing Letters , 19 : 4515705 [ DOI: 10.1109/LGRS.2022.3222185 http://dx.doi.org/10.1109/LGRS.2022.3222185 ]
Yan Z Y , Wang P J , Xu F , Sun X and Diao W H . 2023 . AIR-PV: a benchmark dataset for photovoltaic panels extraction in optical remote sensing imagery . Science China Information Sciences , 66 ( 4 ): 140307 [ DOI: 10.1007/s11432-022-3663-1 http://dx.doi.org/10.1007/s11432-022-3663-1 ]
Yang J T , Qiu X L , Ding C B and Lei B . 2018 . Identification of stable backscattering features, suitable for maintaining absolute synthetic aperture radar (SAR) radiometric calibration of sentinel-1 . Remote Sensing , 10 ( 7 ): 1010 [ DOI: 10.3390/rs10071010 http://dx.doi.org/10.3390/rs10071010 ]
Yu K , Liu S H and Zhao Y C . 2016 . CPBAC: a quick atmospheric correction method using the topographic information . Remote Sensing of Environment , 186 : 262 - 274 [ DOI: 10.1016/j.rse.2016.08.010 http://dx.doi.org/10.1016/j.rse.2016.08.010 ]
Zhang A D , Wu M Q , Wang D P , Cao J R and Jia W H . 2014 . The Basis and Application of Remote Sensing Technology . Beijing : Science Press
张安定 , 吴孟泉 , 王大鹏 , 曹建荣 , 贾维花 . 2014 . 遥感技术基础与应用 . 北京 : 科学出版社
Zhou G R , Xu Z Q , Fan Y Z , Zhang Z , Qiu X L , Zhang B C , Fu K and Wu Y R . 2023 . HPHR-SAR-Net: Hyper high-resolution SAR imaging network based on nonlocal total variation . arXiv preprint arXiv : 2304 . 04428 [ DOI: 10.48550/arXiv.2304.04428 http://dx.doi.org/10.48550/arXiv.2304.04428 ]
Zhu S J , Lei B and Wu Y R . 2018 . Retrieval of hyperspectral surface reflectance based on machine learning . Remote Sensing , 10 ( 2 ): 323 [ DOI: 10.3390/rs10020323 http://dx.doi.org/10.3390/rs10020323 ]
Zhu S J , Lei B and Wu Y R . 2019 . Automatic color correction for remote sensing optical image based on dense convolutional networks . Journal of University of Chinese Academy of Sciences , 36 ( 1 ): 93 - 100
朱思捷 , 雷斌 , 吴一戎 . 2019 . 基于稠密卷积神经网络的遥感图像自动色彩校正 . 中国科学院大学学报 , 36 ( 1 ): 93 - 100 [ DOI: 10.7523/j.issn.2095-6134.2019.01.013 http://dx.doi.org/10.7523/j.issn.2095-6134.2019.01.013 ]
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相关作者
邓力维 中国地质大学 计算机学院;桂林理工大学 测绘地理信息学院
张洪艳 中国地质大学 计算机学院
付波霖 桂林理工大学 测绘地理信息学院;中国地质大学 计算机学院
库闵凡 湖北大学 资源环境学院
张良 湖北大学 资源环境学院;天津市轨道交通导航定位及时空大数据技术重点实验室;中国铁路设计集团有限公司
邓继伟 天津市轨道交通导航定位及时空大数据技术重点实验室;中国铁路设计集团有限公司
王广帅 天津市轨道交通导航定位及时空大数据技术重点实验室;中国铁路设计集团有限公司
王新文 湖北大学 资源环境学院
相关机构
中国地质大学 计算机学院
桂林理工大学 测绘地理信息学院
湖北大学 资源环境学院
天津市轨道交通导航定位及时空大数据技术重点实验室
中国铁路设计集团有限公司
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