地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (9): 1080-1091.doi: 10.3724/SP.J.1047.2015.01080
收稿日期:
2014-12-12
修回日期:
2015-02-14
出版日期:
2015-09-10
发布日期:
2015-09-07
通讯作者:
郑逢斌
E-mail:ly.sci.art@gmail.com;zhengfb@henu.edu.cn
作者简介:
作者简介:刘 扬(1971-),男,河南信阳人,副教授,博士,主要从事媒体神经认知计算、时空信息高性能计算。E-mail:
基金资助:
LIU Yang1,2,3(), FU Zhengye4, ZHENG Fengbin2,3,*(
)
Received:
2014-12-12
Revised:
2015-02-14
Online:
2015-09-10
Published:
2015-09-07
Contact:
ZHENG Fengbin
E-mail:ly.sci.art@gmail.com;zhengfb@henu.edu.cn
About author:
*The author: SHEN Jingwei, E-mail:
摘要:
高分辨率遥感影像的目标分类与识别,是对地观测系统进行图像分析理解,以及自动目标识别系统提取目标信息的重要手段。本文综述了当前国内外在可见光、红外、合成孔径雷达和合成孔径声纳等遥感影像的目标分类与识别的关键技术和最新研究进展。首先,讨论了高分辨率遥感影像的目标分类与识别问题的主要研究层次和内容;其次,深入分析了高分辨率遥感影像目标分类与识别,在滤波降噪、特征提取、目标检测、场景分类、目标分类和目标识别的关键技术及其所存在的问题;最后,结合并行计算、神经计算和认知计算等技术,讨论了目标分类与识别的可行性方案。具体包括:(1)高性能并行计算在高分辨率遥感图像处理的主流技术,并给出了基于Hadoop+OpenMP+CUDA的高分辨率遥感影像混合并行处理架构;(2)深度学习对于提升目标分类和识别精度的应用前景,以及基于深度神经网络的多层次遥感影像目标识别方法;(3)认知计算在解决遥感影像大数据不确定性分析的模型与算法,并讨论了层次主题模型的多尺度遥感影像场景描述方案。此外,根据媒体神经认知计算的相关研究,探讨了遥感影像大数据的目标分类和识别的发展趋势和研究方向。
刘扬, 付征叶, 郑逢斌. 高分辨率遥感影像目标分类与识别研究进展[J]. 地球信息科学学报, 2015, 17(9): 1080-1091.DOI:10.3724/SP.J.1047.2015.01080
LIU Yang,FU Zhengye,ZHENG Fengbin. Review on High Resolution Remote Sensing Image Classification and Recognition[J]. Journal of Geo-information Science, 2015, 17(9): 1080-1091.DOI:10.3724/SP.J.1047.2015.01080
[1] | 郭华东,王力哲,陈方,等.科学大数据与数字地球[J].科学通报,2014,59(12):1047-1054. |
[2] | 李德仁,姚远,邵振峰.智慧城市中的大数据[J].武汉大学学报(信息科学版),2014,39(6):631-640. |
[3] | 李德毅,郑思仪.大数据时代的创新思维[J].北京联合大学学报,2014,28(4):1-6. |
[4] | 李小文. 汶川震灾中遥感的应急与反思[J].遥感学报,2008,12(6):838. |
[5] | Di Bisceglie M, Galdi C.CFAR detection of extended objects in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005,43(4):833-843. |
[6] | Zhao H Y, Wang Q, Huang J J, et al.Method for inshore ship detection based on feature recognition and adaptive background window[J]. Journal of Applied Remote Sensing, 2014,8(1):1-14. |
[7] | 冯卫东,孙显,王宏琦.基于空间语义模型的高分辨率遥感图像目标检测方法[J].电子与信息学报,2013,35(10):2518-2523. |
[8] | 何楚,张宇,廖紫纤,等.基于分层自适应部分模型的遥感图像飞机目标检测[J].武汉大学学报(信息科学版),2013,38(6):656-660. |
[9] | 蒋李兵,王壮,郁文贤.基于属性滤波和上下文分析的高分辨遥感图像建筑物提取方法[J].电子与信息学报,2012,34(12):2985-2991. |
[10] | 李湘眷,孙显,王宏琦.基于多核学习的高分辨率遥感图像目标检测方法[J].测绘科学,2013,38(5):84-87. |
[11] | Han J W, Zhou P C, Zhang D W, et al.Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,89(1):37-48. |
[12] | Zhang L F, Zhang L P, Tao D C, et al.Sparse Transfer Manifold Embedding for Hyperspectral Target Detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(2):1030-1043. |
[13] | Bosch A, Zisserman A, Munoz X.Scene classification using a hybrid generative/discriminative approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008,30(4):712-727. |
[14] | Zhao L J, Tang P, Huo L Z.A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification[J]. International Journal of Remote Sensing, 2014,35(6):2296-2310. |
[15] | Li J, Liu Z.The Study of Scene Classification in the Multisensor Remote Sensing Image Fusion[J]. Mathematical Problems in Engineering, 2013,2013(2013):1-10. |
[16] | Guo L, Chehata N, Mallet C, et al.Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011,66(1):56-66. |
[17] | Cheng G, Guo L, Zhao T Y, et al.Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA[J]. International Journal of Remote Sensing, 2013,34(1):45-59. |
[18] | Zhao B, Zhong Y F, Zhang L P.Scene classification via latent Dirichlet allocation using a hybrid generative/discriminative strategy for high spatial resolution remote sensing imagery[J]. Remote Sensing Letters, 2013,4(12):1204-1213. |
[19] | Gong Y, Wang L, Guo R, et al.Multi-scale orderless pooling of deep convolutional activation features[C]. 13th European Conference on Computer Vision (ECCV), SEP 06-12, 2014:392-407. |
[20] | Williams D P, Fakiris E.Exploiting environmental information for improved underwater target classification in Sonar imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(10):6284-6297. |
[21] | 殷君君,安文韬,杨健.基于极化散射参数与Fisher-OPCE的监督目标分类[J].清华大学学报(自然科学版),2011,1(12):1782-1786. |
[22] | Marconcini M, Fernandez-Prieto D.A novel approach to targeted land-cover classification of remote-sensing images[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012:7345-7348. |
[23] | He W, Zhou W, Su P.Research on Classification and Target Recognition of Remote Sensing Image Based on Improved Support Vector Machine[J]. Remote Sensing Information, 2010(6):6-8,13. |
[24] | Dai D X, Yang W.Satellite Image Classification via two-layer sparse coding with biased image representation[J]. IEEE Geoscience and Remote Sensing Letters, 2011,8(1):173-176. |
[25] | Luo L, Hu Y.Explorations on object-oriented classification for ground targets from high-resolution image[C]. International Forum on Information Technology and Applications (IFITA 2009), 2009:139-143. |
[26] | 李雪轲,王晋年,张立福,等.面向对象的航空高光谱图像混合分类方法[J].地球信息科学学报, 2014,16(6):941-948. |
[27] | Blaschke T, Hay G J, Kelly M, et al.Geographic object-based image analysis -towards a new paradigm[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014,87(1):180-191. |
[28] | 李德仁,童庆禧,李荣兴,等.高分辨率对地观测的若干前沿科学问题[J].中国科学:地球科学,2012,42(6):805-813. |
[29] | Zhang L F, Zhang L P, Tao D C, et al.A multifeature tensor for remote-sensing target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2011,8(2):374-378. |
[30] | Guo W Y, Xia X Z, Wang X F.A remote sensing ship recognition method based on dynamic probability generative model[J]. Expert Systems with Applications, 2014,41(14):6446-6458. |
[31] | 丁正虎,余映,王斌,等.选择性视觉注意机制下的多光谱图像舰船检测[J].计算机辅助设计与图形学学报,2011, 23(3):419-425. |
[32] | Lu C Y, Zou H X, Sun H, et al.Combing rough set and RBF neural network for large-scale ship recognition in optical satellite images[J]. 35th International Symposium on Remote Sensing of Environment (ISESE35),2014,17(1):1-6. |
[33] | Prasad S, Bruce L M.Decision fusion with confidence-based weight assignment for hyperspectral target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008,46(5):1448-1456. |
[34] | Sun J D, Fan G L, Yu L J, et al.Concave-convex local binary features for automatic target recognition in infrared imagery[J]. Eurasip Journal on Image and Video Processing, 2014, 2014(23):1-13. |
[35] | Wang M G, Tian Y G.Target recognition of infrared bridge image based on morphological operator[C]. International Conference on Advances in Engineering (ICAE), 2011:490-494. |
[36] | Lang H T, Zhang J, Zhang T, et al.Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery[J]. Journal of Applied Remote Sensing, 2014,8(1):1-17. |
[37] | Bryner D, Srivastava A.Shadow segmentation in SAS and SAR using bayesian elastic contours[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2013:375-380. |
[38] | Lopera O, Dupont Y.Automated target recognition with SAS: Shadow and highlight-based classification[C]. 2012 Oceans, 2012:1-5. |
[39] | Isaacs J C, Tucker J D.Signal diffusion features for automatic target recognition in synthetic aperture sonar[C]. Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE), 2011 IEEE, 2011:461-465. |
[40] | Fei T, Kraus D, Zoubir A M.Contributions to automatic target recognition systems for underwater mine classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015,53(1):505-518. |
[41] | Wang X Y, Li Z H, Gao S.Parallel remote sensing image processing: Taking image classification as an example[C]. 6th International Symposium, ISICA 2012, October 27-28, 2012:159-169. |
[42] | Yang J H, Zhang J X, Huang G M.A parallel computing paradigm for pan-sharpening algorithms of remotely sensed images on a multi-core computer[J]. Remote Sensing, 2014,6(7):6039-6063. |
[43] | Qu H C, Zhang J P, Chen Y S, et al.Parallel implementation for SAM algorithm based on GPU and distributed computing[C]. 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2012:4074-4077. |
[44] | Yang C T, Huang C L, Lin C F. Hybrid CUDA, OpenMP, and MPI parallel programming on multicore GPU clusters[J]. Computer Physics Communications, 2011,182(1):266-269. |
[45] | Timchenko L, Yarovyy A, Kokriatskaia N, et al.Application of parallel-hierarchical transformations for rapid recognition of dynamic images based on GPU technology[C]. Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013), 2013:224-228. |
[46] | Chen H, Liu X Y, Shao S, et al.A GPU-paralleled implementation of an enhanced face recognition algorithm[C]. Proc. SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, 2013:1-10. |
[47] | Li G Q, Liu D S.Key technologies research on building a cluster-based parallel computing system for remote sensing[C]. Computational Science - ICCS 2005, Pt 3, 2005:484-491. |
[48] | Jing X Y, Li S, Zhang D, et al.Supervised and unsupervised parallel subspace learning for large-scale image recognition[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2012,22(10):1497-1511. |
[49] | 王珊,王会举,覃雄派,等.架构大数据:挑战、现状与展望[J].计算机学报,2011,34(10):1741-1752. |
[50] | Taylor R C.An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics[C]. Proceedings of the 11th Annual Bioinformatics Open Source Conference (BOSC) 2010, July 2010:1-6. |
[51] | Dean J, Ghemawat S.MapReduce: A flexible data processing tool[J]. Communications of the ACM, 2010,53(1):72-77. |
[52] | Lv Z H, Hu Y J, Zhong H D, et al.Parallel K-means clustering of remote sensing images based on MapReduce[C]. Web Information Systems and Mining 2010, October 23-24, 2010:162-170. |
[53] | 李帆,何洪林,任小丽,等.基于MapReduce的空间敏感性分析并行算法设计[J].地球信息科学学报, 2014,16(6):874-881. |
[54] | Ghosh-Dastidar S, Adeli H.Spiking neural networks[J]. International Journal of Neural Systems, 2009,9(4):295-308. |
[55] | Liu Y D, Wang L M.Application of memristor-based spiking neural network in image edge extraction[J]. Acta Physica Sinica, 2014,63(8):1-7. |
[56] | Wu Q X, McGinnity T M, Maguire L, et al. A visual attention model based on hierarchical spiking neural networks[J]. Neurocomputing, 2013,116(1):3-12. |
[57] | 蔺想红,张田文,张贵仓.进化大规模脉冲神经网络的发育方法[J].计算机学报,2012,35(12):2633-2644. |
[58] | Lee H, Grosse R, Ranganath R, et al.Unsupervised learning of hierarchical representations with convolutional deep belief networks[J]. Communications of the ACM, 2011,54(10): 95-103. |
[59] | Le Q V, Ranzato M A, Monga R, et al.Building high-level features using large scale unsupervised learning[C]. 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013:8595-8598. |
[60] | Dahl G E, Yu D, Deng L, et al.Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012,20(1):30-42. |
[61] | Masci J, Meier U, Ciresan D, et al.Stacked convolutional auto-encoders for hierarchical feature extraction[C]. Artificial Neural Networks and Machine Learning - ICANN 2011, Pt I, 2011:52-59. |
[62] | Smirnov E A, Timoshenko D M, Andrianov S N.Comparison of regularization methods for ImageNet classification with deep convolutional neural networks[C]. 2nd AASRI Conference on Computational Intelligence and Bioinformatics (CIB), 2014:89-94. |
[63] | Chen Y S, Lin Z H, Zhao X, et al.Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014,7(6):2094-2107. |
[64] | Gondek D C, Lally A, Kalyanpur A, et al.A framework for merging and ranking of answers in DeepQA[J]. IBM Journal of Research and Development, 2012,56(3-4):1-12. |
[65] | Ogiela L, Ogiela M R, Tadeusiewicz R.Ubiquitous computing in creation of cognitive systems for medical images interpretation[C]. International Conference on Ubiquitous Computing and Multimedia Applications, JUN 23-25, 2010:44-50. |
[66] | 骆剑承. 遥感影像智能图解及其地学认知问题探索[J].地理科学进展,2000,19(4):289-296. |
[67] | Wang L Z, Lu K, Liu P, et al.IK-SVD: Dictionary learning for spatial big data via incremental atom update[J]. Computing in Science & Engineering, 2014,16(4):41-52. |
[68] | Luo W, Li H L, Liu G H.Automatic annotation of multispectral satellite images using author-topic Model[J]. IEEE Geoscience and Remote Sensing Letters, 2012,9(4):634-638. |
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