浏览全部资源
扫码关注微信
Received：02 January 2021，
Published：07 March 2021
移动端阅览
中国对地观测数据获取量持续增加，已步入遥感大数据时代，开展多星一体化数据处理与应用已成为发展趋势。文章从成像处理与信息提取两个方面系统回顾了技术发展过程，剖析了现有前沿方法的优势和特点，指出成像处理领域建立统一处理模型、信息提取领域建立高效学习模型面临的主要挑战。在此基础上，结合实际应用需求，提出了遥感大数据条件下多星一体化处理与分析的新思路，重点阐述了多星一体化处理与分析的基本概念、科学问题与解决方案，通过构建基于生成对抗网络的多星一体化成像处理模型与记忆保持的多任务特征共享与小样本增量学习模型，综合利用不同卫星和载荷间、不同任务和目标间信息，实现高质量图像产品的生成与高精度标注结果的提取。文末给出了技术途径和初步试验验证，并展望后续技术攻关方向。
With the rapid development of aerospace technology and the continuous increase of China’s high resolution earth observation data acquisition in the past few decades
the era of remote sensing big data has coming now. Developing multi-satellite integrated data processing and application technology has become an important trend. In this paper
we systematically review the technological development process from two aspects
including multi-satellite imaging processing and multi-element information extraction. Then
we analyze the advantages and characteristics of the existing cutting-edge methods
and points out the main challenge of establishing the integrated processing model for imaging processing field and efficient learning model for information extraction field. On this basis
as well as according to the practical application requirements
a novel method of multi-satellite integrated processing and analysis under remote sensing big data is proposed in this paper. We emphatically define the basic concepts
scientific problems
research ideas and solutions of this method. On one hand
in terms of multi-satellite imaging processing
aiming at the problem of difficult estimation of high-dimensional coupled imaging parameters at high resolution
the various errors caused by the whole cycles of electromagnetic waves are calculated quantitatively
including payload error
platform error
data transmission error
atmospheric influence and so on. Then a multi-satellite integrated imaging processing physical model based on the Generative Adversarial Networks (GAN) is constructed to approximate estimate imaging parameters. In this way
we can achieve high-precision geometric corrections and radiation corrections
as well as generate high-quality remote sensing image products. On the other hand
in terms of multi-element information extraction
to solve the problem that the accuracy of the original tasks is difficult to maintain due to the addition of new tasks and requires full sample retraining
we design a multi-task feature sharing model based on few-shot incremental learning. It has a novel memory retention unit and multi-modal joint optimization of convex non-negative matrix factors. Through this model
we can generate parallel and high-precision annotations for multiple objects. Compared with the existing methods
the information between different satellites and payloads
different missions and objects are complementary to each other
leading to the simultaneous improvement of multi-sensor imaging quality and object extraction accuracy. The specific technical approach and preliminary experimental verification are given in detail in this paper. Experiments on multi-satellite imaging processing show that our method can effectively estimate multiple imaging errors. The phase estimation accuracy is within 1 degree when the signal-to-noise ratio is -5dB
which indicates the good performance even under low signal-to-noise ratio condition. At the same time
experiments on multi-element information extraction show that compared to single-modal method
our method
which uses multi-modal data in combination and embedded the novel memory retention unit
improve the extraction accuracy in multi-tasks of object detection and semantic segmentation. In the future
we will pay more attention to the disconnection problem between multi-satellite imaging processing and multi-element information extraction in the field of remote sensing. By establishing a benign mutual feedback mechanism between these two procedures to maximize the benefits of the remote sensing big data.
Bai J . 2019 . Launch of Zhuhai-1 Group 03 Satellite [2021-01-20] . http://www.chinanews.com/gn/2019/09-19/8960059.shtml http://www.chinanews.com/gn/2019/09-19/8960059.shtml
Bamler R . 1992 . A comparison of range-doppler and wavenumber domain SAR focusing algorithms . IEEE Transactions on Geoscience & Remote Sensing , 30 ( 4 ): 706 - 713 [ DOI: 10.1109/36.158864 http://dx.doi.org/10.1109/36.158864 ]
Bennett J R and Cumming I G . 1979 . A digital processor for the production of seasat synthetic aperture radar imagery . Proc. SURGE Workshop , ( 154 ): 16 - 18 .
Benson M , Pierce L and Sarabandi K . 2017 . Estimating the three-dimensional structure of the harvard forest using a database driven multi-modal remote sensing technique . IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , 5814 - 5817 .
Breit H , Börner E , Mittermayer J , Holzner J and Eineder M . 2004 . The TerraSAR-X multi-mode sar processor algorithms and design. Eusar, Ulm, May . DLR .
Bresnahan P C . 2011 . Geolocation accuracy evaluations of commercial satellite imagery: challenges and results . The 34th Annual AAS Guidance and Control Conference .
Brockmann Consult GmbH . 2019 . Calvalus Processing System [2021-01-25] . https://www.brockmann-consult.de/portfolio/big-data-in-earth-observation/ https://www.brockmann-consult.de/portfolio/big-data-in-earth-observation/
Chen K , Fu K , Gao X , Yan M and Sun X . 2019 . Effective fusion of multi-modal data with group convolutions for semantic segmentation of aerial imagery . IEEE International Geoscience and Remote Sensing Symposium , 3911 - 3914 .
Chen Q , He F , Yu A , Dong Z and Liang D . 2012 . A multi-mode space-borne synthetic aperture radar signal processor . International Conference on Signal Processing (ICSP), Beijing, China : 2040 - 2043 [ DOI: 10.1109/ICoSP.2012.6491982 http://dx.doi.org/10.1109/ICoSP.2012.6491982 ]
Chen Y , Li G , Zhang Q and Sun J . 2017 . Refocusing of moving targets in SAR images via parametric sparse representation . Remote Sensing , 9 ( 8 ): 795 [ DOI: 10.3390/rs9080795 http://dx.doi.org/10.3390/rs9080795 ]
Cheng C Q , Zhang J X , Huang G M , Zhang L and Yang J H . 2017 . Combined positioning of TerraSAR-X and SPOT-5 HRS images with RFM considering accuracy information of orientation parameters . Acta Geodaetica et Cartographica Sinica , 46 ( 2 ): 179 - 187
程春泉 , 张继贤 , 黄国满 , 张力 , 杨景辉 . 2017 . 考虑定向参数精度信息的TerraSAR-X和SPOT-5 HRS影像RFM联合定位 . 测绘学报 , 46 ( 2 ): 179- 187 [ DOI: 10.11947/j.AGCS.2017.2016 0138 http://dx.doi.org/10.11947/j.AGCS.2017.20160138 ]
Cui L , Qiu X L , Guo J Y , Wen X J , Yang J Y and Fu K . 2020 . 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 ]
Ding C B , Liu J Y , Lei B and Qiu X L . 2017 . Preliminary exploration of systematic geolocation accuracy of gf-3 sar satellite system . Journal of Radars , 6 ( 1 ): 11 - 16
丁赤飚 , 刘佳音 , 雷斌 , 仇晓兰 . 2017 . 高分三号SAR卫星系统级几何定位精度初探 . 雷达学报 , 6 ( 1 ): 11- 16 [ DOI: 10.12000/JR17024 http://dx.doi.org/10.12000/JR17024 ]
Fernandez-Beltran R , Haut J M , Paoletti M E , Plaza J , Plaza A and Pla F . 2018 . Remote sensing image fusion using hierarchical multimodal probabilistic latent semantic analysis . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 11 ( 12 ): 4982 - 4993 [ DOI: 10.1109/JSTARS.2018.28813 42 http://dx.doi.org/10.1109/JSTARS.2018.2881342 ]
Grodecki J and Dial G . 2003 . Block adjustment of high-resolution satellite images described by rational polynomials . Photogrammetric Engineering & Remote Sensing , 9 ( 7 ): 12 - 28 . [ DOI: 10.14358/PERS.69.1.59 http://dx.doi.org/10.14358/PERS.69.1.59 ]
Helder D , Thome K J . Mishra N , Chander G and Choi T . 2013 . Absolute radiometric calibration of landsat using a pseudo invariant calibration site . IEEE Transactions on Geoscience and Remote Sensing , 51 ( 3 ): 1360 - 1369 . [ DOI: 10.1109/TGRS.2013.2243738 http://dx.doi.org/10.1109/TGRS.2013.2243738 ]
Hu F and Jin S Y . 2017 . Analysis on the development of high-resolution optical remote sensing satellite wide-format imaging technology . Geomatics World , 24 ( 5 ): 45 - 50
胡芬 , 金淑英 . 2017 . 高分辨率光学遥感卫星宽幅成像技术发展浅析 . 地理信息世界 , 24 ( 5 ): 45- 50 [ DOI: cnki:sun:chrk.0.2017-05-010 http://dx.doi.org/cnki:sun:chrk.0.2017-05-010 ]
Jeong J , Yang C and Kim T . 2015 . Geo-positioning accuracy using multiple-satellite images: Ikonos, QuickBird, and Kompsat-2 stereo images . Remote Sensing , 7 ( 4 ): 4549 - 4564 [ DOI: 10.3390/rs70404549 http://dx.doi.org/10.3390/rs70404549 ]
Jiao N G , Wang F , You H J , Qiu X L and Yang M D . 2019 . Geolocation accuracy improvement of multiobserved GF-3 spaceborne SAR imagery . IEEE Geoscience and Remote Sensing Letters , ( 99 ): 1 - 5 [ DOI: 10.1109/LGRS.2019.2955491 http://dx.doi.org/10.1109/LGRS.2019.2955491 ]
Jin H H . 2020 . Look at the Huoshenshan hospital from 500 kilometers above [2021-01-20] . http://news.china.com.cn/txt/2020-01/31/content_75662044.htm http://news.china.com.cn/txt/2020-01/31/content_75662044.htm
Kampffmeyer M , Salberg A B , Jenssen R . 2016 . Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks . 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV , 2016 , pp. 680 - 688 . [ DOI: 10.1109/CVPRW.2016.90 http://dx.doi.org/10.1109/CVPRW.2016.90 ]
Khalel A , Tasar O , Charpiat G and Tarabalka Y . 2019 . Multi-task deep learning for satellite image pansharpening and segmentation . 2019 IEEE International Geoscience and Remote Sensing Symposium , 4869 - 4872 .
Kusk A , Abulaitijiang A and Dall J . 2016 . Synthetic SAR image generation using sensor, terrain and target models . Proceedings of EUSAR 2016 : 11th European Conference on Synthetic Aperture Radar . Hamburg, Germany: VDE : 405 - 409 .
Leberl F . 1973 . Radargrammetry for Image Interpretation . Paris : International Institute for Aerial Survey and Earth .
Li C , Shen Y , Li B , Gang Q , Liu S , Wang W and Tong X . 2014 . An improved geopositioning model of QuickBird high resolution satellite imagery by compensating spatial correlated errors . ISPRS Journal of Photogrammetry and Remote Sensing 96 : 12 - 19 . [ DOI: 10.1016/j.isprsjprs.2014.06.010 http://dx.doi.org/10.1016/j.isprsjprs.2014.06.010 ]
Li J , Zhang H and Zhang L . 2015 . Efficient superpixel-oriented multi-task joint sparse representation classification for hyperspectral imagery . 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , 2592 - 2595 .
Li K , Wan G , Cheng G , Meng L and Han J . 2019 . Object detection in optical remote sensing images: A survey and a new benchmark . ISPRS Journal of Photogrammetry and Remote Sensing , 159 : 296 - 307 [ DOI: 10.1016/j.isprsjprs.2019.11.023 http://dx.doi.org/10.1016/j.isprsjprs.2019.11.023 ]
Li R , Zhou F , Niu X and Di K . 2007 . Integration of Ikonos and QuickBird imagery for geopositioning accuracy analysis . Photogrammetric Engineering and Remote Sensing , 73 ( 9 ): 1067 - 1074 [ DOI: 10.2112/05-0470.1 http://dx.doi.org/10.2112/05-0470.1 ]
Li S Y , Zhang W F and Yang S . 2017 . Intelligence fusion method research of multisource high-resolution remote sensing images . Journal of Remote sensing , 21 ( 3 ): 415 - 424
李盛阳 , 张万峰 , 杨松 . 2017 . 多源高分辨率遥感影像智能融合 . 遥感学报 , 21 ( 3 ): 415- 424 [ DOI: 10.11834/jrs.20176386 http://dx.doi.org/10.11834/jrs.20176386 ]
Liang J . 2017 . Gaojing-1 satellite imagery parameters [2021-02-02] . http://www.ce.cn/xwzx/gnsz/gdxw/201703/15/t20170315_20997952.shtml http://www.ce.cn/xwzx/gnsz/gdxw/201703/15/t20170315_20997952.shtml
Liebel L , Bittner K and Krner M . 2020 . A generalized multi-task learning approach to stereo DSM filtering in urban areas . ISPRS Journal of Photogrammetry and Remote Sensing 166 : 213 - 227 [ DOI: 10.1016/j.isprsjprs.2020.03.005 http://dx.doi.org/10.1016/j.isprsjprs.2020.03.005 ]
Liu L , Yao F and Wang H . 2020 . 2020 Global space launch reports [2021-01-16] . https://mp.weixin.qq.com/s/BEdGo7X5NPFhFrAY_ YZnRA https://mp.weixin.qq.com/s/BEdGo7X5NPFhFrAY_YZnRA
Liu Y , Minh Nguyen D , Deligiannis N , Ding W and Munteanu A . 2017 . Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery . Remote Sensing , 9 ( 6 ): 522 - 535 [ DOI: 10.3390/rs9060522 http://dx.doi.org/10.3390/rs9060522 ]
Longbotham N , Pacifici F , Glenn T , Zare A , Volpi M , Tuia D , Christophe E , Michel J , Inglada J and Chanussot J . 2012 . Multi-modal change detection, application to the detection of flooded areas: outcome of the 2009–2010 data fusion contest . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 5 ( 1 ): 331 - 342 [ DOI: 10.1109/JSTARS.2011.2179638 http://dx.doi.org/10.1109/JSTARS.2011.2179638 ]
Luo C and Ma L . 2018 . Manifold regularized distribution adaptation for classification of remote sensing images . IEEE Access , 6 : 4697 - 4708 [ DOI: 10.1109/access.2018.2789932 http://dx.doi.org/10.1109/access.2018.2789932 ]
Lyu G N , Tang X M , Ai B , Li T and Chen Q F . 2018 . Hybrid geometric calibration method for multi-platform spaceborne sar image with sparse gcps . Acta Geodaetica et Cartographica Sinica , 47 ( 7 ): 986 - 995
吕冠南 , 唐新明 , 艾波 , 李涛 , 陈乾福 . 2018 . 稀少控制的多平台星载SAR联合几何定标方法 . 测绘学报 , 47 ( 7 ): 986- 995 [ DOI: 10.11947/j.AGCS.2018.20170283 http://dx.doi.org/10.11947/j.AGCS.2018.20170283 ]
Ma D A , Tang P and Zhao L J . 2018 . SiftingGAN: generating and sifting labeled samples to improve the remote sensing image scene classification baseline in vitro . IEEE Geoscience and Remote Sensing Letters , 16 ( 7 ): 1046 - 1050 [ DOI: 10.1109/lgrs.2018.2890413 http://dx.doi.org/10.1109/lgrs.2018.2890413 ]
Malmgren-Hansen D , Kusk A , Dall J and Nielsen A . 2017 . Engholm R and Skriver H . Improving SAR automatic target recognition models with transfer learning from simulated data. IEEE Geoscience and Remote Sensing Letters , 14 ( 9 ): 1484 - 1488 [ DOI: 10.1109/lgrs.2017.2717486 http://dx.doi.org/10.1109/lgrs.2017.2717486 ]
Meng D , Ding C , Hu D , Qiu X , Huang L , Han B , Liu J and Xu N . 2017 . On the processing of very high resolution spaceborne SAR data:a chirp-modulated back projection approach . IEEE Transactions on Geoscience and Remote Sensing , 99 : 1 - 11 [ DOI: 10.1109/TGRS.2017.2744649 http://dx.doi.org/10.1109/TGRS.2017.2744649 ]
Meng D , Hu D and Ding C . 2015 . Precise focusing of airborne SAR data with wide apertures large trajectory deviations:a chirp modulated back-projection approach . IEEE Transactions on Geoscience and Remote Sensing , 53 ( 5 ): 2510 - 2519 [ DOI: 10.1109/TGRS.2014.2361134 http://dx.doi.org/10.1109/TGRS.2014.2361134 ]
Nan K , Qi H and Ye Y X . 2019 . A template matching method of multimodal remote sensing images based on deep convolutional feature representation . Acta Geodaetica et Cartographica Sinica , 48 ( 6 ): 727 - 736
南轲 , 齐华 , 叶沅鑫 . 2019 . 深度卷积特征表达的多模态遥感影像模板匹配方法 . 测绘学报 , 48 ( 6 ): 727- 736 [ DOI: 10.11947/j.AGCS.2019.20180432 http://dx.doi.org/10.11947/j.AGCS.2019.20180432 ]
Pan H B , Zhang G , Tang X M , Wang X , Zhou P , Xu M Z and Li D R . 2013 . Analysis and verification of the accuracy of the image products of the zy-3 surveying and mapping satellite . Acta Geodaetica et Cartographica Sinica , 042 ( 005 ): 738 - 744
潘红播 , 张过 , 唐新明 , 王霞 , 周平 , 许妙忠 , 李德仁 . 2013 . 资源三号测绘卫星影像产品精度分析与验证 . 测绘学报 , 042 ( 005 ): 738- 744 [ DOI: cnki:sun:chxb.0.2013-04-010 http://dx.doi.org/cnki:sun:chxb.0.2013-04-010 ]
Papadomanolaki M , Karantzalos K and Vakalopoulou M . 2019 . A multi-task deep learning framework coupling semantic segmentation and image reconstruction for very high-resolution imagery . 2019 IEEE International Geoscience and Remote Sensing Symposium .
Papoulis A . 1968 . System and transforms with applications in optics . New York : McGraw-Hill .
Qiu X , Hu D and Ding C . 2008 . An improved NLCS algorithm with capability analysis for one-stationary BiSAR . IEEE Transactions on Geoscience & Remote Sensing , 46 ( 10 ): 3179 - 3186 [ DOI: 10.1109/TGRS.2008.921569 http://dx.doi.org/10.1109/TGRS.2008.921569 ]
Rao M B , Tang P and Zhang Z . 2019 . Spatial-spectral relation network for hyperspectral image classification with limited training samples . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 12 ( 12 ): 5086 - 5100 [ DOI: 10.1109/jstars.2019.2957047 http://dx.doi.org/10.1109/jstars.2019.2957047 ]
Rosa L , Zortea M , Gemignani B H , Oliveira D and Feitosa R . 2020 . FCRN-based multi-task learning for automatic citrus tree detection from uav images . 2020 IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS) , 403 - 408 [ DOI: 10.5194/isprs-annals-IV-3-W2-2020-65-2020 http://dx.doi.org/10.5194/isprs-annals-IV-3-W2-2020-65-2020 ]
Shao Q , Wang S Y , Yang C X and Zhang X H . 2017 . Landsat 5 tm , landsat 7 etm+ and landsat 8 oli cross calibration research . Industry and Technology Forum , 16 ( 017 ): 54 - 57
邵琦 , 王思懿 , 杨春熙 , 张雪红 . 2017 . Landsat 5 tm,landsat 7 etm+及landsat 8 oli交叉定标研究 . 产业与科技论坛 , 16 ( 017 ): 54 - 57
Shi J , Shao T , Liu X , Zhang X and Lei Y . 2020 . Evolutionary multi-task ensemble learning model for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 99 : 1 - 15 [10.1109/JSTARS . 2020 . 3037353 ]
Tang S , Wu B , Zhu Q . 2016 . Combined adjustment of multi-resolution satellite imagery for improved geo-positioning accuracy . ISPRS Journal of Photogrammetry & Remote Sensing , 114 : 125 - 136 [ DOI: 10.1016/j.isprsjprs.2016.02.003 http://dx.doi.org/10.1016/j.isprsjprs.2016.02.003 ]
Thome K , Mccorkel J and Czapla-Myers J . 2013 . In-situ transfer standard and coincident-view intercomparisons for sensor cross-calibration . IEEE Transactions on Geoscience and Remote Sensing , 51 ( 3 ): 1088 - 1097 . [ DOI: 10.1109/TGRS.2013.2243841 http://dx.doi.org/10.1109/TGRS.2013.2243841 ]
UCS satellite database [ 2021-01-15 ]. https://www.ucsusa.org/sites/default/files/2020-10/UCS-Satellite-Database-8-1-2020.xls https://www.ucsusa.org/sites/default/files/2020-10/UCS-Satellite-Database-8-1-2020.xls
Viktor M S and Kenneth C . 2013 . Big Data: A Revolution That Will Transform How We Live, Work and Think . London : Hodder [ DOI: 10.1080/1369118X.2014.923482 http://dx.doi.org/10.1080/1369118X.2014.923482 ]
Volpi M and Tuia D . 2018 . Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images . ISPRS journal of photogrammetry and remote sensing , 144 : 48 - 60 [ DOI: 10.1016/j.isprsjprs.2018.06.007 http://dx.doi.org/10.1016/j.isprsjprs.2018.06.007 ]
Wahl D E and Eichel P H . 1994 . Phase gradient autofocus-a robust tool for high resolution SAR phase correction . IEEE Transactions on Aerospace and Electronic Systems , 30 ( 3 ): 827 - 834 [ DOI: 10.1109/7.303752 http://dx.doi.org/10.1109/7.303752 ]
Wang Y , Ding W , Zhang R and Li H . 2020 . Boundary-aware multi-task learning for remote sensing imagery . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , ( 99 ): 1 - 15 [ DOI: 10.1109/JSTARS.2020.3043442 http://dx.doi.org/10.1109/JSTARS.2020.3043442 ]
Wei Z Q . 2001 . Synthetic aperture radar satellite . Beijing: Science Press. (魏钟铨 . 2001 . 合成孔径雷达卫星 . 北京 ： 科学出版社 .)
Wivell C E , Steinwand D R , Kelly G G and Meyer D J . 2002 . Evaluation of terrain models for the geocoding and terrain correction, of synthetic aperture radar (SAR) images . IEEE Transactions on Geoscience & Remote Sensing , 30 ( 6 ), 1137 - 1144 [ DOI: 10.1109/36.193789 http://dx.doi.org/10.1109/36.193789 ]
Wu Y D and Ming Y . 2012 . Multi-source SAR image joint positioning method lacking ground control points . Journal of Hubei University of Technology . 27 ( 4 ): 13 - 17 .
吴颖丹 , 明洋 . 2012 . 缺少地面控制点的多源SAR影像联合定位方法 . 湖北工业大学学报 , 27 ( 4 ): 13- 17 [ DOI: 10.3969/j.issn.1003-4684.2012.04.027 http://dx.doi.org/10.3969/j.issn.1003-4684.2012.04.027 ]
Xinhua News Agency . 2020 . Through the clouds and soaking rain! High-resolution satellite hits the red flood warning of the Yangtze River [2021-01-12] . https://baijiahao.baidu.com/s?id=167229470 7973973457&wfr=spider&for=pc https://baijiahao.baidu.com/s?id=1672294707973973457&wfr=spider&for=pc
Yang L , Fu Q Y , Pan Z Q , Zhang X W , Han Q J and Liu L . 2015 . Research on Radiation Cross Calibration of Gaofen-1 Satellite Camera . Infrared and Laser Engineering , 44 ( 8 ): 2456 - 2456
杨磊 , 傅俏燕 , 潘志强 , 张学文 , 韩启金 , 刘李 . 2015 . 高分一号卫星相机的辐射交叉定标研究 . 红外与激光工程 , 44 ( 8 ): 2456- 2456 [ DOI: 10.3969/j.issn.1007-2276.2015.08.036 http://dx.doi.org/10.3969/j.issn.1007-2276.2015.08.036 ]
Yan Y M , Tan Z C and Su N . 2019 . A data augmentation strategy based on simulated samples for ship detection in rgb remote sensing images . ISPRS International Journal of Geo-Information , 8 ( 6 ): 276 [ DOI: 10.3390/ijgi8060276 http://dx.doi.org/10.3390/ijgi8060276 ]
Zhan Y , Hu D , Wang Y T and Yu X C . 2017 . Semisupervised hyperspectral image classification based on generative adversarial networks . IEEE Geoscience and Remote Sensing Letters , 15 ( 2 ): 212 - 216 [ DOI: 10.1109/lgrs.2017.2780890 http://dx.doi.org/10.1109/lgrs.2017.2780890 ]
Zhang L , Dong H and Zou B . 2019 . Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework . ISPRS Journal of Photogrammetry and Remote Sensing , 157 : 59 - 72 [ DOI: 10.1016/j.isprsjprs.2019.09.002 http://dx.doi.org/10.1016/j.isprsjprs.2019.09.002 ]
Zhang G , Li Z , Pan H B , Qiang Q and Zhai L . 2011 . Orientation of spaceborne SAR stereo pairs employing the RPC adjustment model . IEEE Transactions on Geoscience and Remote Sensing , 49 ( 7 ): 2782 - 2792 [ DOI: 10.1109/TGRS.2011.2107559 http://dx.doi.org/10.1109/TGRS.2011.2107559 ]
Zhang S W . 2020 . More than 30 satellites escort the fight against the epidemic [2021-01-20] . http://news.sciencenet.cn/htmlnews/2020/3/437404.shtm http://news.sciencenet.cn/htmlnews/2020/3/437404.shtm .
Zhang Y , Du B , Zhang L and Liu T . 2016 . Joint sparse representation and multitask learning for hyperspectral target detection . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 2 ): 894 - 906 [ DOI: 10.1109/TGRS.2016.2616649 http://dx.doi.org/10.1109/TGRS.2016.2616649 ]
Zhang Y H , Lin Z J , Zhang J X and Gan M L . 2002 . SAR image geometric correction . Acta Geodaetica et Cartographica Sinica , 31 ( 2 ): 134 - 138
张永红 , 林宗坚 , 张继贤 , 甘梦龙 . 2002 . SAR影像几何校正 . 测绘学报 , 31 ( 2 ): 134- 138 [ DOI: 10.3321/j.issn:1001-1595.2002.02.009 http://dx.doi.org/10.3321/j.issn:1001-1595.2002.02.009 ]
Zhang Y H , Zhong B , Yang F J and Liu Q H . 2012 . BRDF feature extraction based on TM/ETM and DEM data . Journal of Remote Sensing , 16 ( 2 ): 361 - 377
张玉环 , 仲波 , 杨锋杰 , 柳钦火 . 2012 . 基于TM/ETM和DEM数据的BRDF特征提取 . 遥感学报 , 16 ( 2 ): 361- 377 [ DOI: 10.11834/jrs.20102423 http://dx.doi.org/10.11834/jrs.20102423 ]
Zhang Y H , Sun H , Zuo J W , Wang H Q , Xu G L and Sun X . 2018 . Aircraft type recognition in remote sensing images based on feature learning with conditional generative adversarial networks . Remote Sensing , 10 ( 7 ): 1123 [ DOI: 10.3390/rs10071123 http://dx.doi.org/10.3390/rs10071123 ]
Zhou X and Prasad S . 2018 . Deep feature alignment neural networks for domain adaptation of hyperspectral data . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 10 ): 5863 - 5872 [ DOI: 10.1109/tgrs.2018.2827308 http://dx.doi.org/10.1109/tgrs.2018.2827308 ]
Zhu L , Chen Y S , Ghamisi P and Benediktsson J A . 2018 . Generative adversarial networks for hyperspectral image classification . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 9 ): 5046 - 5063 [ DOI: 10.1109/tgrs.2018.2805286 http://dx.doi.org/10.1109/tgrs.2018.2805286 ]
相关文章
相关作者
相关机构
京公网安备11010802024621