Performance Analysis of Image Fusion Techniques to Improve Quality of Satellite Data

Authors

  • Bhagyamma S  Department of Electronics & Communication Engineering, GEC, Kushalnagar, Kodagu, Karnataka, India
  • A L Choodarathnakara  Department of Electronics & Communication Engineering, GEC, Kushalnagar, Kodagu, Karnataka, India
  • Ranjitha T K  Department of Electronics & Communication Engineering, GEC, Kushalnagar, Kodagu, Karnataka, India
  • Ramya A P  Department of Electronics & Communication Engineering, GEC, Kushalnagar, Kodagu, Karnataka, India
  • Niranjan K M  Department of Electronics & Communication Engineering, GEC, Kushalnagar, Kodagu, Karnataka, India

Keywords:

Remote sensing, Image Fusion, BT, PCA, HPF, Ehlers, Modified-HIS

Abstract

Satellite Image data are now collected with different spatial, spectral, and temporal resolutions. Image fusion techniques are used extensively to combine different images having complementary information into one signal composite. The fused image has rich information that will improve the performance of image analysis algorithms. Pansharpening is a pixel level fusion technique used to increase the spatial resolution of the multispectral image using spatial information from the high resolution panchromatic image while preserving the spectral information in the multispectral image. Resolution merge, image integration, and multisensory data fusion are some of the equivalent terms used for pansharpening. Pansharpening techniques are applied for enhancing certain features not visible in either of the single data alone, change detection using temporal data sets, improving geometric correction, and enhancing classification. This paper mainly focus on incorporating various image fusion techniques that are available in the literature. Using commercial remote sensing software package Erdas Imagine V9.2. The performance of these image fusion techniques varies both spectrally and spatially. Hence, in this work qualitative and quantitative metrics for evaluating the quality of pansharpend images have been analysed. For this study, the principal component analysis, Brovay sharpening method, High pass filter, Ehlers method and Modified IHS methods are used.

References

  1. Robert A. Schowengerdt, “Remote sensing: Models and methods for image processing”, second edition, Elsevier, 2006.
  2. B. C. Panda, “Remote sensing: Principles and applications in remote sensing”, Pearson education, 1995.
  3. Susmitha Vekkot and Pancham Shukla, “A Novel Architecture for Wavelet based Image Fusion”, World Academy of Science, Engineering and Technology 57 2009.
  4. S. G. Nikolov, “Multisensors Image Fusion techniques in Remote Sensing”, ISPRS Journal of Photogrammetry and Remote Sensing, 46 (1), 19-30, 1991.
  5. Brown L. G, “A Survey of Image Registration Techniques”, ACM Computing surveys, Vol. 24, No. 4, pp. 325-376, 1992.
  6. YAO Wang-quang and Zhang, “Multiscale Contrast Image Fusion scheme with performance measure”, Optica application, Vol. XXXIV, NO. 3, 2004.
  7. Kirankumar, “Comparison of Wavelets and Conventional Image Fusion Methods”, IEEE Transactions on Image Processing, 3:248-251, 1995.
  8. Y. Kiran Kumar, “Comparison of Fusion Technique applied to preclinical images: Fast Discrete Curvelet Transform using Wrapping Technique & Wavelet Transform”, Journal of Theoretical and Applied Information Technology, 668-674, 2005-2009.
  9. Roger L. KING, “A Challenge for High Spatial, Spectral and Temporal Resolution Data Fusion”, IEEE 2602-2605, 2000.
  10. E. J. Stollnitz, T. D. De Rose, D. H. Salesin, “Wavelets for Computer Graphics: a primer”, Part 1, IEEE Comput. Graphics 76-84, Appl. 15 (3) (1995).
  11. J. Liu, Q. Wang and Y. Shen, “Comparisons of several Pixel-Level Image Fusion schemes for Infrared and Visible light Images,” in Proceedings of the IEEE Instrumentation and Measurement Technology Conference, Vol. 3, Ottawa, Canada, pp. 2024 2027, May 2005.
  12. Gemma Piella and Henk Heijmans, “Multiresolution Fusion guided by a Multimodal Segmentation which shows a Region based Fusion”, Proceedings of ACIVS Ghent Belgium Sep-9-11, 2002.
  13. D. A. Yocky, “Image Merging and Data Fusion by means of the Discrete two-dimensional Wavelet Transform,” J. Opt. Soc. Amer. A, Vol. 12, No. 9, pp. 1834-1841, 1995
  14. J. Nunez, X. Otazu, O. Fors, A. Prades, “Simultaneous Image Fusion and Reconstruction using Wavelets Applications to SPOT + LANDSAT Images”, Vistas Astron., 41 351-357, 1997.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2017-06-30

Issue

Volume 3, Issue 3, May-June-2017

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Bhagyamma S, A L Choodarathnakara, Ranjitha T K, Ramya A P, Niranjan K M "Performance Analysis of Image Fusion Techniques to Improve Quality of Satellite Data" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 3, pp.314-326, May-June-2017.