A Novel Method of Ship Detection from Spaceborne Optical Image Based on Spatial Pyramid Matching

Jun Guo; Chang Ren Zhu

doi:10.4028/www.scientific.net/AMM.190-191.1099

Paper Titles

The Design of Power Equipment Monitoring System Based on Wireless Sensor Network
p.1079

The Research on Multi-Level Theses Evaluation Model Based on Fuzzy Cluster Analysis
p.1083

Vehicle Detection Based on Binocular Stereovision and Edge Extraction
p.1090

A Laser Projection Device for Locating Points on the Plane Object
p.1094

A Novel Method of Ship Detection from Spaceborne Optical Image Based on Spatial Pyramid Matching
p.1099

A Remote Control System Based on Short Messaging Service for Intelligent Agricultural Irrigation
p.1104

An Improved Edge Extraction Algorithm for Lithography Micrographs
p.1109

Design and Research of the Gas Monitoring System Based on ZigBee Wireless Network
p.1113

Design of Colliery Gas Monitoring Alarm System Based on DSP
p.1117

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 190-191A Novel Method of Ship Detection from Spaceborne...

A Novel Method of Ship Detection from Spaceborne Optical Image Based on Spatial Pyramid Matching

Abstract:

In this paper we propose an automatic ship detection method in High Resolution optical satellite images based on neighbor context information. First, a pre-detection of targets gives us candidates. For each candidate, we choose an extended region called candidate with neighborhood which comprises candidate and its neighbor area. Second, the patches of candidate with neighborhood are got by a regular grid, and their SIFT(Scale Invariant Feature Transform) features are extracted. Then the SIFT features of training images are clustered with the K-means algorithm to form a codebook of the patches. We quantize the patches of candidate with neighborhood according to this codebook and get the visual word representation. Finally by applying spatial pyramid matching, the candidates are classified with SVM (support vector machine). Experiment results are given for a set of images show that our method has got predominant performance.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 190-191)

Pages:

1099-1103

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.190-191.1099

Citation:

Cite this paper

Online since:

July 2012

Authors:

Jun Guo, Chang Ren Zhu

Keywords:

Context Information, Remote Sensing, Ship Detection, Spatial Pyramid Matching

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

References

[1] G. Margarit, J. J. Mallorqui, J. Fortuny-Guasch, C. Lopez-Martinez, Exploitation of ship scattering in polarimetric SAR for an improved classification under high clutter conditions, IEEE Transactions on Geoscience and Remote Sensing 47(4) (2009).

DOI: 10.1109/tgrs.2008.2008721

Google Scholar

[2] C. Liu, C.H. Gierull, A new application for PolSAR imagery in the field of moving target indication/ship detection, IEEE Transactions on Geoscience and Remote Sensing 45(11) (2007) 3426-3436.

DOI: 10.1109/tgrs.2007.907192

Google Scholar

[3] S. Lazebnik, C. Schmid, and J. Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, CVPR (2006).

DOI: 10.1109/cvpr.2006.68

Google Scholar

[4] K. Grauman and T. Darrell, Pyramid match kernels: Discriminative classification with sets of image features, ICCV (2005).

DOI: 10.1109/iccv.2005.239

Google Scholar

[5] N. Proia and V. Pagé, Characterization of a bayesian ship detection method in optical satellite images, IEEE Geoscience and Remote Sensing Letters 7(2) (2010) 226-230.

DOI: 10.1109/lgrs.2009.2031826

Google Scholar

[6] C. Zhu, H. Zhou, R. Wang, J. Guo, A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features, IEEE Geoscience and Remote Sensing 48(9) (2010) 3446-3456.

DOI: 10.1109/tgrs.2010.2046330

Google Scholar

[7] D.G. Lowe, Distinctive image features from scale-invariant keypoints, Int. J. Comput. Vis. 60 (2004) 91-110.

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[8] D. Casasent，W. Su，D. Turaga. SAR Ship Detection Using New Conditional Contrast box Filter, SPIE Conference on Algorithm for Synthetic Aperture Radar Imagery, Orlando，Florida, (1999) 274-284.

DOI: 10.1117/12.357645

Google Scholar

[9] T. Blaschke, Object based image analysis for remote sensing, ISPRS International Journal of Photogrammetry and Remote Sensing 65(1) (2010) 2-16.

DOI: 10.1016/j.isprsjprs.2009.06.004

Google Scholar

[10] V. Vapnik. Statistical Leaning Theory, New York, Wiley, (1998).

Google Scholar

[11] J. Qin, N. Yung, Scene categorization via contextual visual words, Pattern Recognition 43 (2010) 1874-1888.

DOI: 10.1016/j.patcog.2009.11.009

Google Scholar

[12] A. Oliva, A. Torralba, The role of context in object recognition, Trends in Cognitive Sciences 11(12) (2007) 520–527.

DOI: 10.1016/j.tics.2007.09.009

Google Scholar