Current Science

Open Access Open Access  Restricted Access Subscription Access

Fusion of Multispectral and Panchromatic Data using Regionally Weighted Principal Component Analysis and Wavelet


Affiliations
1 Department of Electronics and Communication Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru 570 016,, India
2 SDM Institute of Technology, Ujire, Belthangady 574 240, India
3 Department of Electronics and Communication Engineering, Malnad College of Engineering, Hassan 573 202, India
 

This study proposes a new multispectral (MS) and panchromatic (PAN) image fusion algorithm based on regionally weighted principal component analysis (RW-PCA) and wavelet. First, the MS images are segmented into spectrally similar regions based on the fuzzy c-means (FCM) clustering method. Secondly, based on the spectral vector’s degree of membership in each region, a new RW-PCA method is proposed to fuse the MS and PAN images region by region, and fused MS images are obtained. In the traditional PCA-based fusion method, the MS and PAN images are fused globally with the same transform method. In the proposed RW-PCA-based fusion method, the local spectrum information of the MS images is employed, and the spectral information is better preserved in the fused MS images. Finally, in order to improve the quality of spectral and spatial details, the above fused MS images and the original PAN images are further fused using the wavelet-based fusion method, and the final fused MS images are obtained. Experimental results demonstrated that the proposed image fusion algorithm performs better in spectral preservation and spatial quality improvement than some other methods do.

Keywords

Fuzzy, RWPCA_WT, Regionally Weighted, WT.
User
Notifications
Font Size

  • Jayanth, J. and Shivaprakash Koliwad, Performance degraded by the sensor noise at pixel level image fusion. Int. J. Comput. Appl., 2010, 8(9), 23–28.

  • Jayanth J, Ashok Kumar, T. and Shiva Prakash Koliwad, Comparative analysis of image fusion techniques for remote sensing. International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2012) Cairo, Egypt, 8–10 December 2012. Proc. Commun. Comput. Inf. Sci. (eds Hassanien, A. E. et al.), Springer, Berlin/Heidelberg, Germany, 2012, 322, 111–117.

  • Wang, M. et al., Satellite jitter detection and compensation using multispectral imagery. Remote Sens. Lett., 2016, 7(6), 513–522.

  • Wang, M., Shadow compensation algorithm for remote sensing images based on RGB and HSI color space. Geospat. Inf., 2014, 61, 403–407.

  • Jayanth, J. T., Ashok Kumar, T. and Shiva Prakash Koliwad, Classification of remote sensed data using artificial bee colony algorithm. Egypt. J. Remote Sens. Space Sci. (EJRS, ISSN:1110-9823), 2015, 7(1); doi:10.1016/j.ejrs.2015.03.001.

  • Mirzapour, F. and Ghassemian, H., Improving hyperspectral image classification by combining spectral, texture, and shape features. Int. J. Remote Sens., 2015, 36(4), 1070–1096.

  • Golipour, M., Ghassemian, H. and Mirzapour, F., Integrating hierarchical segmentation maps with MRF prior for classification of hyperspectral images in a Bayesian framework. IEEE Trans. Geosci. Remote Sens., 2016, 54(2), 805–816.

  • Shahdoosti, H. R. and Ghassemian, H., Fusion of MS and PAN images preserving spectral quality. IEEE Geosci. Remote Sens. Lett., 2015, 12(3), 611–615.

  • Luo, Y., Liu, R. and Feng Zhu, Y., Fusion of remote sensing image base on the PCA + ATROUS wavelet transform. Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci., 2008, XXXVII(Part B7), 1155–1158.

  • Jayanth, J., Ashok Kumar, T. and Shiva Prakash Koliwad, Image fusion using DWT + IHS for coastal region change detection. Proceedings of the International Conference Current Trends Engineering Management (ICCTEM), 12–14 July 2012, VVCE, Mysore, Karnataka, India, pp. 26–31.

  • Cheng, J., Liu, H., Liu, T., Wang, F. and Li, H., Remote sensing image fusion via wavelet transform and sparse representation. ISPRS J. Photogramm. Remote Sens., 2015, 24(2), 158–173.

  • Jayanth, J. Ashok Kumar, T. and Shiva Prakash Koliwad, Six different image fusion techniques for remote sensed data. Proceedings of the International Conference on Communication, VLSI and Signal Processing (ICCVSP), 20–22 February 2013, SIT, Tumkur, India, pp. 22–25.

  • Liu, J., Huang, J., Liu, S., Li, H., Zhou, Q. and Liu, J., Human visual system consistent quality assessment for remote sensing image fusion. ISPRS J. Photogramm. Remote Sens., 2015, 105, 79–90.

  • Jagalingam, P. and Vittal Hegde, A., A review of quality metrics for fused image. In International Conference on Water Resources, Coastal and Ocean Engineering, 2015, pp. 133–142.

  • Song, H., Huang, B., Liu, Q. and Zhang, K., Improving the spatial resolution of land-sat TM/ETM + through fusion with SPOT5 images via learning based super-resolution. IEEE Trans. Geosci. Remote Sens., 2015, 53(3), 1195–1204.

  • Mirzapour, F. and Ghassemian, H., Improving hyperspectral image classification by combining spectral, texture, and shape features. Int. J. Remote Sens., 2015, 36(4), 1070–1096.

  • Pohl, C. and Van Genderen, J. L., Structuring contemporary remote sensing image fusion. Int. J. Image Data Fus., 2016, 6(1), 3–21.

  • Huang, X., Wen, D., Xie, J. and Zhang, L., Quality assessment of panchromatic and multispectral image fusion for the ZY-3 satellite: from an information extraction perspective. IEEE Geosci. Remote Sens. Lett., 2014, 11(4), 753–757.


Abstract Views: 330

PDF Views: 132




  • Fusion of Multispectral and Panchromatic Data using Regionally Weighted Principal Component Analysis and Wavelet

Abstract Views: 330  |  PDF Views: 132

Authors

J. Jayanth
Department of Electronics and Communication Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru 570 016,, India
T. Ashok Kumar
SDM Institute of Technology, Ujire, Belthangady 574 240, India
Shivaprakash Koliwad
Department of Electronics and Communication Engineering, Malnad College of Engineering, Hassan 573 202, India

Abstract


This study proposes a new multispectral (MS) and panchromatic (PAN) image fusion algorithm based on regionally weighted principal component analysis (RW-PCA) and wavelet. First, the MS images are segmented into spectrally similar regions based on the fuzzy c-means (FCM) clustering method. Secondly, based on the spectral vector’s degree of membership in each region, a new RW-PCA method is proposed to fuse the MS and PAN images region by region, and fused MS images are obtained. In the traditional PCA-based fusion method, the MS and PAN images are fused globally with the same transform method. In the proposed RW-PCA-based fusion method, the local spectrum information of the MS images is employed, and the spectral information is better preserved in the fused MS images. Finally, in order to improve the quality of spectral and spatial details, the above fused MS images and the original PAN images are further fused using the wavelet-based fusion method, and the final fused MS images are obtained. Experimental results demonstrated that the proposed image fusion algorithm performs better in spectral preservation and spatial quality improvement than some other methods do.

Keywords


Fuzzy, RWPCA_WT, Regionally Weighted, WT.

References





DOI: https://doi.org/10.18520/cs%2Fv115%2Fi10%2F1938-1942