Fusion of Multispectral and Panchromatic Images Based on Entropy and Fruit Fly Optimization

Fusion of Multispectral and Panchromatic Images Based on Entropy and Fruit Fly Optimization

Abdelwhab OuahabMohamed F. Belbachir

Laboratoire Signaux, Systèmes et Données (LSSD), Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf, USTO-MB, BP 1505, El M’naouer, Oran 31000, Algérie

Corresponding Author Email: 
11 July 2018
5 September 2018
30 September 2018
| Citation



Image fusion (pan-sharpening) aims to generate a single image from both a panchromatic (PAN) image and multispectral images (MS).   The pan-sharpened images ought to be identical to the MS images in terms of spectral information and ought also to be similar to the PAN image in terms of spatial information. Different fusion methods and algorithms have been purposed in the literature such as intensity-hue-saturation (IHS), wavelet transform (WT), principal component analysis (PCA) and Brovey transform (BT), etc. These techniques can produce color distortions in the fused images. This problem is principally due to the fact that the same details extracted from the PAN image are injected into each band of the MS images. FUFSER method utilizes the spectral response functions and Fourier transform (FT) to make an injection model. A new fusion method based on FUFSER method is presented in order to improve the spatial and spectral qualities of the fused images. This method usees local and global parameters to compute the amount of spatial details extracted from the PAN image to be added into each band of the MS images. The global parameters are computed using the fruit fly optimization, whereas the local parameters are computed using the entropy. The proposed method is applied to Pléiades and IKONOS images and compared with some existing fusion methods. The results obtained showed that the proposed method has better performance compared than other methods in terms of spatial and spectral information. 


image fusion, pansharpening, entropy, fruit fly optimization

1. Introduction
2. Fruit Fly Optimization (FFO)
3. The Proposed Fusion Method
4. Experiment Results
5. Conclusions

[1] Lari SN, Yazdi M. (2016). Improved IHS pan-sharpening method based on adaptive injection of à trous wavelet decomposition. International Journal of Signal Processing, Image Processing and Pattern Recognition. 9(3): 291-308. https://doi.org/10.14257/ijsip.2016.9.3.26

[2] Zhang Y. (2004). Understanding image fusion. Photogramm. Eng. Remote Sens. 70(6): 657-661.

[3] Thomas C, Ranchin T, Wald L, Chanussot J. (2008). Synthesis of multispectral images to high spatial resolution: a critical review of fusion methods based on remote sensing physics. IEEE Trans. Geosci. and Remote Sens. 46(5): 1301-1312. https://doi.org/10.1109/TGRS.2007.912448

[4] Panchal1 S, Thakker R. (2015). Implementation and comparative quantitative assessment of different multispectral image pansharpening approaches. Signal & Image Processing: An International Journal (SIPIJ). 6(5). https://doi.org/10.5121/sipij.2015.6503

[5] Mu T, Su SC, Shyu HS, Huang S. (2001). A new look at IHS like image fusion methods. Info. Fusion. 2(3):177-186. https://doi.org/10.1016/S1566-2535(01)00036-7

[6] Hanping M, Yancheng Z, Xinzhong W. Fusion algorithm for multi-sensor images based on PCA and lifting wavelet transformation. New Zealand Journal of Agricultural 50(5): 667-671. https://doi.org/10.1080/00288230709510336

[7] Akoguz A. (2014) Pansharpening of multispectral images using filtering in Fourier domain. image and signal processing for remote sensing. Proceeding of 2014, SPIE 9244, 92441X. https://doi.org/10.1117/12.2067255.

[8] Otazu X, González-Audícana M, Fors O. (2005). Introduction of sensor spectral response into image fusion methods. IEEE Trans. Geosci. Remote Sens. 43(10): 2376-2385. https://doi.org/10.1109/TGRS.2005.863299

[9] Ghellab A, Belbachir MF. (2013) Efficient image fusion method based on the Fourier transform by introducing sensor spectral response. J. Appl. Remote Sens. 7(1): 073552. https://doi.org/10.1117/1.JRS.7.073552

[10] Wang Z, Bovik AC, Sheik HR, Simoncelli EP. (2014). Image Quality Assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing. 13(4): 600-612. https://doi.org/10.1109/TIP.2003.819861

[11] Zhou J, Tang BG, Ren XW. (2017) Research on prediction model for icing thickness of transmission lines based on BP neural network optimized with improved fruit fly algorithm. AMSE Journal-AMSE IIETA Publication 60: 255-269. https://doi.org/10.18280/ama_b.600116

[12] Pan W. (2012) A new fruit fly optimization algorithm Taking the financial distress model as an example. Knowledge-Based Systems 26: 69-74. https://doi.org/10.1016/j.knosys.2011.07.001

[13] Wang Z, Ziou D, Armenakis C, Li D, Li Q. (2005). A comparative analysis of image fusion methods. IEEE Trans. Geosci. Remote Sens. 43(6): 1391-1402. https://doi.org/10.1109/TGRS.2005.846874

[14] Park JH, Kang MG. (2004). Spatially adaptive multi-resolution multispectral image fusion. Int J Remote Sens 25(23): 5491-5508. https://doi.org/10.1080/01431160412331270830

[15] Choi J, Han D, Kim Y (2012). Context-adaptive pansharpening algorithm for high-resolution satellite imagery. Can J Remote Sensing 38(1): 109-124. https://doi.org/10.5589/m12-015

[16] Kim Y, Eo Y. (2011). Generalized IHS-based satellite imagery fusion using spectral response functions. ETRI J. 33(4): 497-505. https://doi.org/10.4218/etrij.11.1610.0042

[17] Roberts JW, Aardt JV, Ahmed F. (2008). Assessment of image fusion procedures using entropy, image quality, and multispectral classification. J Appl Remote Sens. 2(1): 023522. https://doi.org/10.1117/1.2945910

[18] Chavez PS, Kwarteng AW. (1989). Extracting spectral contrast in Landsat thematic mapper image data using selective principal component analysis. Photogrammetric Engineering and Remote Sensing. 55(3): 339-348. 

[19] Vivone G, Alparone L, Chanussot J. (2015). A critical comparison among pan sharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing 53(5): 2565-2586. https://doi.org/10.1109/TGRS.2014.2361734.

[20] Núñez J. (1999). Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans Geosci Remote Sens 37(3): 1204-1211.

[21] Aiazzi B, Baronti S, Selva M. (2007). Improving component substitution pansharpening through multivariate regression of MS+Pan data. IEEE Trans Geosci Remote Sens 45(10): 3230-3239. https://doi.org/10.1109/TGRS.2007.901007

[22] Palubinskas G. (2013) Fast, simple, and good pan-sharpening method. J. Appl. Remote Sens. 7(1): 073526. 


[23] Srikanth B, Kumar H, Rao K. (2018) A robust approach for WSN localization for underground coal mine monitoring using improved RSSI technique. Mathematical Modelling of Engineering Problems 5(3): 225-231. https://doi.org/10.18280/mmep.050314

[24] Wald L, Ranchin T, Mangolini M. (1997) Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images. Amer Soc Photogramm Remote Sens. 63(6): 691-699.

[25] Wang Z, Bovik AC, Sheik HR, Simoncelli EP. (2004) Image Quality Assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4): 600-612. https://doi.org/10.1109/TIP.2003.819861