A Method of High-resolution Remote Sensing ImageRetrieval Based on LDA

SHEN Sheng-yu; LIU Zhe; ZHANG Ping-cang; ZHANG Tong; WU Hua-yi; CHEN Xiao-ping

doi:10.3969/j.issn.1001-5485.2014.08.0192014,31(08):98-102,121

PDF(2456 KB)

Journal of Changjiang River Scientific Research Institute ›› 2014, Vol. 31 ›› Issue (8) : 98-102. DOI: 10.3969/j.issn.1001-5485.2014.08.0192014,31(08):98-102,121

INFORMATION TECHNOLOGY APPLICATION

A Method of High-resolution Remote Sensing ImageRetrieval Based on LDA

SHEN Sheng-yu¹, LIU Zhe², ZHANG Ping-cang¹, ZHANG Tong³, WU Hua-yi³, CHEN Xiao-ping¹

Author information +

History +

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.

Key words

LDA / high-resolution remote sensing image / remote sensing image retrieval / visual features

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

SHEN Sheng-yu, LIU Zhe, ZHANG Ping-cang, ZHANG Tong, WU Hua-yi, CHEN Xiao-ping. A Method of High-resolution Remote Sensing ImageRetrieval Based on LDA[J]. Journal of Changjiang River Scientific Research Institute. 2014, 31(8): 98-102 https://doi.org/10.3969/j.issn.1001-5485.2014.08.0192014,31(08):98-102,121

References

[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.