传统遥感影像检索存在时间和人工成本高、内容信息描述太粗略等问题,更未充分考虑语义信息,难以应对高分辨率遥感影像的海量地物类型及其复杂关系。借鉴信息检索的思想,引入计算机视觉领域的视觉特征和自然语言处理领域的概率主题模型,提出了一种基于LDA(Latent Dirichlet Allocation)的高分辨率遥感影像检索方法。通过一组多主题个数的高分辨率遥感影像检索实验证明,该方法在主题个数较少时,能达到较好的检索效果,较高的查准率,而且在主题个数继续增加时,能使查准率保持在0.9左右。
Abstract
Traditional methods of remote sensing image retrieval cannot handle high-resolution images with huge amounts of surface feature types and complex relations. It requires high cost of time and labor, gives sketchy content description, and cannot adequately consider the semantic information. Inspired by text information retrieval, the visual features in computer vision and the probabilistic topic model in natural language processing are introduced to present a method of retrieving high-resolution remote sensing image based on LDA (Latent Dirichlet Allocation). Retrieval experiments on high resolution remote sensing images with multiple topic numbers suggest that even when the number of topic is small, good retrieval results and high precision can be achieved. As the topic number increases, the precision rate remains at about 0.9.
关键词
LDA /
高分辨率遥感影像 /
遥感影像检索 /
视觉特征
Key words
LDA /
high-resolution remote sensing image /
remote sensing image retrieval /
visual features
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参考文献
[1] 朱先强. 融合视觉显著特征的遥感图像检索研究[D]. 武汉:武汉大学, 2011. (ZHU Xian-qiang. Remote Sensing Imagery Retrieval Based on Integrating Visual Saliency Features[D]. Wuhan: Wuhan University, 2011.(in Chinese))
[2] 李小文. 定量遥感的发展与创新[J]. 河南大学学报(自然科学版), 2005, 35(4): 49-56.(LI Xiao-wen. Retrospect, Prospect and Innovation in Quantitative Remote Sensing[J]. Journal of Henan University(Natural Science), 2005, 35(4): 49-56. (in Chinese))
[3] MANNING C D, RAGHAVAN P, SCHUTZE H. Introduction to Information Retrieval [M]. Cambridge, England: Cambridge University Press, 2009.
[4] DEERWESTER S, DUMAIS S T, FURNAS G W, et al. Indexing by Latent Semantic Analysis [J]. Journal of the American Society for Information Science, 1990, 41(6): 391-407.
[5] HOFMANN T. Probabilistic Latent Semantic Analysis [C]∥ Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, July 30-August 1, 1999: 289-296.
[6] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet Allocation [J]. Journal of Machine Learning Research, 2003, 3(4/5): 993-1022.
[7] STEYVERS M, GRIFFITHS T. Probabilistic Topic Models In Handbook of Latent Semantic Analysis [M]. UK: Lawrence Erlbaum Associates, 2007.
[8] SIVIC J, RUSSELL B C, EFROS A A, et al. Discovering Objects and Their Location in Images[C]∥Proceedings of the International Conference on Computer Vision, Beijing, China, October 17-21, 2005: 370-377.
[9] MATAS J, CHUM O, URBAN M, et al. Robust Wide Baseline Stereo from Maximally Stable Extremal Regions[C]∥Proceedings of the 13th British Machine Vision Conference, Cardiff, September 2-5, 2002: 384-396.
[10]MIKOLAJCZYK K, SCHMID C. An Affine Invariant Interest Point Detector[C]∥Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002: 128-142.[11]MIKOLAJCZYK K, SCHMID C. Scale & Affine Invariant Interest Point Detectors[J]. International Journal of Computer Vision, 2004, 60(1): 63-86.
[12]KADIR T, ZISSERMAN A, BRADY M. An Affine Invariant Salient Region Detector[C]∥Proceedings of the European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004: 228-241.
[13]LOWE G D. Object Recognition from Local Scale-Invariant Features [C]∥Proceedings of the International Conference on Computer Vision, Corfu, Greece, September 20-25, 1999: 1150-1157.
[14]BROWN P F, PIETRA V J D, MERCER R L, et al. An Estimate of an Upper Bound for the Entropy of English [J]. Computational Linguistics, 1992, 18(1): 31-40.
[15]VGG. Affine Covariant Regions [EB/OL]. (2007-07-15) [2013-05-10] http:∥www.robots.ox.ac.uk/~vgg/research/affine/index.html.
基金
国家自然科学基金资助项目(41271400);国家973计划资助项目(2012CB719906);中央级公益性科研院所基本科研业务费(CKSF2014024/TB,CKSF2012055/TB,CKSF2012044/TB)
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