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Reseach Article

An Approach for Object Detection in Multi Temporal Aerial Images

by Tapasmini Sahoo, Kunal Kumar Das
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 10
Year of Publication: 2016
Authors: Tapasmini Sahoo, Kunal Kumar Das
10.5120/ijca2016907961

Tapasmini Sahoo, Kunal Kumar Das . An Approach for Object Detection in Multi Temporal Aerial Images. International Journal of Computer Applications. 133, 10 ( January 2016), 44-48. DOI=10.5120/ijca2016907961

@article{ 10.5120/ijca2016907961,
author = { Tapasmini Sahoo, Kunal Kumar Das },
title = { An Approach for Object Detection in Multi Temporal Aerial Images },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 10 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 44-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number10/23826-2016907961/ },
doi = { 10.5120/ijca2016907961 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:30:51.230707+05:30
%A Tapasmini Sahoo
%A Kunal Kumar Das
%T An Approach for Object Detection in Multi Temporal Aerial Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 10
%P 44-48
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper an image fusion technique is developed to detect manmade structures from multi-temporal images. Proposed method is a hybrid approach. Histogram based spectral analysis is used to remove contamination of clouds and their shadows recursively that integrates complimentary information to form a composite image from multi temporal images. Then the algorithm tries to extract edge information using discrete wavelet approach. In this work a recursive threshold based segmentation approach is used for removal of clouds and their shadows but wavelet transform is adopted for the image fusion to reduce artifacts in the fused image. Further a feature-based fusion rule is used to reduce the computing time. The proposed method is used for building detection and results show that the proposed method performs well.

References
  1. Paul J. Gibson. Introductory Remote Sensing. Principles and Concepts. Rutledge, London, first edition, 2000.
  2. Christine Pohl. Geometric aspects of multisensor image fusion for topographic map updating in the humid tropics. PhD thesis, ITC, 1996.
  3. L.M.T. Carvalho, L.M.G. Fonseca, F. Murtagh, and J.G.P.W. Clevers. Digital change detection with the aid of multiresolution wavelet analysis. International Journal of Remote Sensing, 22(18):3871– 3876, 2001.
  4. R. Todd Ogden. Essential wavelets for statistical applications and data analysis. Birkhauser, Boston, 1997.
  5. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, Digital Image Processing using MATLAB a book of Low Price Edition.
  6. Lucien Wald. Definitions and terms of reference in data fusion. International Archives of Photogrammetry and Remote Sensing, 32(Part 7-4-3 W6), June 1999.
  7. Christine Pohl. Tools and methods for fusion of images of different spatial resolution. International Archives of Photogrammetry and Remote Sensing, 32(Part 7-4-3 W6), June 1999.
  8. Paul M. Mather, University of Nottingham, Computer Processing of Remotely Sensed Images, third edition, John Willy & Sons, Ltd.
  9. Bin Wang, Atsuo Ono, Kanako Maramatsu, and Noburo Fujiwara. Automated detection and removal of clouds and their shadows from landsat TM images. IEICE TRANS. INF.and SYST., E82–D(2), February 1999.
  10. V.K. Mehta, C.M. Hammock, and H. Krim. Data fusion of SSM/I channels using multiresolution wavelet transformation. North Carolina State University
  11. Oliver Rockinger. Pixel–level fusion of image sequences using wavelet frames. In Leeds University Press, editor, Proceedings of the 16 Leeds applied shape research workshop, Alt Moabit 96 A, 1996.Daimler Benz AG. Systems Technology Research, Intelligent Systems Group.
  12. Hong Wang, Zhongliang Jing, and Jianxun Li. Image fusion using non–separable wavelet frame. Chinese Optics Letters, 1(9), September 2003.
  13. Wu Xiuqing, Zhou Rong, Xu Yunxizng, “A Method of Wavelet-Based Edge Detection with Data Fusion for Multiple Images”, Proceedings of the 3rd World Congress on Intelligent Control and Automation, July 2000.
  14. Qi-Ming Qin, Si-Jin Chen, Wen-Jun Wang , De-Zhi Chen, Lin Wang, “The Building Recognition of High Resolution Satellite Remote Sensing Image Based On Wavelet Analysis”, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August 2005.
  15. Xiaoying Jin, Curt H. Davis, “Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information”, EURASIPJournal on Applied Signal Processing 2005:14, 2196–2206.
  16. Sahoo T, Pattnaik S, “Cloud removal in Satellite Image using Auto Associative neural n/w & SWT . IEEE International Conference on Emerging Trends in Engineering and Technology, Nagpur, India July 16-18, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Histogram Wavelet Transform Recursive