Object Detection from the Satellite Images Using Divide and Conquer Model
|International Journal of Computer Science and Engineering|
|© 2014 by SSRG - IJCSE Journal|
|Volume 1 Issue 10|
|Year of Publication : 2014|
|Authors : Lakhwinder Kaur, Er.Vinod Kumar Sharma|
How to Cite?
Lakhwinder Kaur, Er.Vinod Kumar Sharma, "Object Detection from the Satellite Images Using Divide and Conquer Model," SSRG International Journal of Computer Science and Engineering , vol. 1, no. 10, pp. 1-5, 2014. Crossref, https://doi.org/10.14445/23488387/IJCSE-V1I10P103
Object detection is the technique of detection of the object type is sub-type of automatic computer vision. This is a growing research area. Object detection in oceans is called oceanology or oceanological computer vision. In the oceans, the object detection is used to find the information about the ships, Islands and other objects. The ocean imaging is done by the satellites and falls under the SAR or aerial imaging category. In this paper, we are proposing a new method of object detection by using the shape and color analysis, followed by divide and conquer model. The proposed algorithm can be used to detect the crashed aeroplanes, floating containers and many other objects. The proposed system can be used to find any physical object whose colour pattern and shape can be specified (known). Proposed system will be produce accurate results than any existing object detection algorithm. Proposed algorithm will perform more in-depth analysis because it uses the combination three popular approaches: object based analysis, pixel based analysis and shape based analysis
Object based analysis, pixel based analysis, shape analysis, ship detection, debris detection.
1. Liu, Ge, Yasen Zhang, Xinwei Zheng, Xian Sun, Kun Fu, and Hongqi Wang. "A New Method on Inshore Ship Detection in High-Resolution Satellite Images Using Shape and Context Information." Geoscience and Remote Sensing Letters, IEEE 11, no. 3 (2014): 617-621.
2. Mace, Thomas H. "At-sea detection of marine debris: overview of technologies, processes, issues, and options." Marine pollution bulletin 65, no. 1 (2012): 23-27.
3. Iervolino, Pasquale, Martin Cohen, Raffaella Guida, and Philip Whittaker. "Ship-detection in SAR imagery using Low Pulse Repetition Frequency Radar." InEUSAR 2014; 10th European Conference on Synthetic Aperture Radar; Proceedings of, pp. 1-4. VDE, 2014.
4. Agarwal, Shivani, Lionel Sujay Vailshery, Madhumitha Jaganmohan, and Harini Nagendra. "Mapping urban tree species using very high resolution satellite imagery: comparing pixelbased and object-based approaches." ISPRS International Journal of Geo-Information 2, no. 1 (2013): 220-236.
5. Carson, Henry S., Megan R. Lamson, Davis Nakashima, Derek Toloumu, Jan Hafner, Nikolai Maximenko, and Karla J. McDermid. "Tracking the sources and sinks of local marine debris in Hawai ‘i." Marine environmental research 84 (2013): 76-83.
6. Rodriguez, Andres, Vishnu Naresh Boddeti, BVK Vijaya Kumar, and Abhijit Mahalanobis. "Maximum margin correlation filter: A new approach for localization and classification." Image Processing, IEEE Transactions on 22, no. 2 (2013): 631-643.
7. Nagendra, Harini, and Duccio Rocchini. "High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail." Biodiversity and Conservation 17, no. 14 (2008): 3431-3442.
8. Benz, Ursula C., Peter Hofmann, Gregor Willhauck, Iris Lingenfelder, and Markus Heynen. "Multi-resolution, objectoriented fuzzy analysis of remote sensing data for GIS-ready information." ISPRS Journal of photogrammetry and remote sensing 58, no. 3 (2004): 239-258.
9. Boyd, Doreen S., and Giles M. Foody. "An overview of recent remote sensing and GIS based research in ecological informatics." Ecological Informatics 6, no. 1 (2011): 25-36.
10. Nagendra, Harini, Richard Lucas, João Pradinho Honrado, Rob HG Jongman, Cristina Tarantino, Maria Adamo, and Paola Mairota. "Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats." Ecological Indicators 33 (2013): 45-59.
11. Blaschke, Thomas. "Object based image analysis for remote sensing." ISPRS journal of photogrammetry and remote sensing 65, no. 1 (2010): 2-16.
12. Gibbes, Cerian, Sanchayeeta Adhikari, Luke Rostant, Jane Southworth, and Youliang Qiu. "Application of object based classification and high resolution satellite imagery for savanna ecosystem analysis." Remote Sensing 2, no. 12 (2010): 2748- 2772.
13. Wang, Kai, Steven E. Franklin, Xulin Guo, and Marc Cattet. "Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists." Sensors 10, no. 11 (2010): 9647-9667.
14. Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1, pp. I-511. IEEE, 2001.
15. Lienhart, Rainer, and Jochen Maydt. "An extended set of haar-like features for rapid object detection." In Image Processing. 2002. Proceedings. 2002 International Conference on, vol. 1, pp. I-900. IEEE, 2002.
16. Schneiderman, Henry, and Takeo Kanade. "A statistical method for 3D object detection applied to faces and cars." In Computer Vision and Pattern R ecognition, 2000. Proceedings. IEEE Conference on, vol. 1, pp. 746-751. IEEE, 2000.
17. Felzenszwalb, Pedro F., Ross B. Girshick, David McAllester, and Deva Ramanan. "Object detection with discriminatively trained part-based models."Pattern Analysis and Machine Intelligence, IEEE Transactions on 32, no. 9 (2010): 1627-1645.
18. Lienhart, Rainer, Alexander Kuranov, and Vadim Pisarevsky. "Empirical analysis of detection cascades of boosted classifiers for rapid object detection." In Pattern Recognition, pp. 297-304. Springer Berlin Heidelberg, 2003.
19. Gall, Juergen, and Victor Lempitsky. "Class-specific hough forests for object detection." In Decision Forests for Computer Vision and Medical Image Analysis, pp. 143-157. Springer London, 2013.
20. Gall, Juergen, Angela Yao, Nima Razavi, Luc Van Gool, and Victor Lempitsky. "Hough forests for object detection, tracking, and action recognition." Pattern Analysis and Machine Intelligence, IEEE Transactions on 33, no. 11 (2011): 2188- 2202.
21. Liebelt, Joerg, and Cordelia Schmid. "Multi-view object class detection with a 3d geometric model." In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 1688-1695. IEEE, 2010.
22. Felzenszwalb, Pedro F., Ross B. Girshick, and David McAllester. "Cascade object detection with deformable part models." In Computer vision and pattern recognition (CVPR), 2010 IEEE conference on, pp. 2241-2248. IEEE, 2010.
23. Ferrari, Vittorio, Frederic Jurie, and Cordelia Schmid. "From images to shape models for object detection." International Journal of Computer Vision 87, no. 3 (2010): 284-303.