1. REMOTE SENSING IMAGE FUSION APPROACH
BASED ON
BROVEY AND WAVELETS TRANSFORMS
By
Reham Abd El whaba Gharbia, PhD Student
Nuclear Materials authority, Egypt
SRGE 20-5–2014 Cairo Egypt

2. Scientific Research Group in Egypt
www.egyptscience.net

3. Overview
 Introduction
 The Objective
 The Brovey Transform
 The Wavelet Transform
 The Proposed Image Fusion Approach
 Experimental Results
 Conclusion And Future Work

4. Introduction
 Remote sensing has
 a huge amount of data
 different spatial resolution for panchromatic and
multispectral imagery
 For the optimum benefit of these characteristics. It
should be collected in a single image.
 There is no single system offers spatial or multispectral
resolution at the same time.

5. Introduction
 Image fusion is used to combine multi-image information in
one image which is more suitable to human vision or more
adapt to further image processing analysis.
 Recently, image fusion has become one of the focuses in
image processing field

6. The Objective
 Introduces a remote sensing image fusion approach based
on a modified version of the Brovey transform and wavelets
to reduce the spectral distortion in the Brovey transform
and spatial distortion in the wavelet transform.

7. The Brovey Transform
 The basic procedure of the Brovey Transform first multiplies
each MS band by the high-resolution Pan band, and then
divides each product by the sum of the MS bands.

8. The Wavelet Transform
 wavelet image fusion
 decomposed the two input images separately into
approximate coefficients and detailed coefficients.
 high detailed coefficients of the multi-spectral image
are replaced with those of the pan image.
 The new wavelet coefficients of the multi-spectral
image are transformed with the inverse wavelet
transform to obtain the fusion multi-spectral image

9. The Proposed Image Fusion Approach
 Preprocessing stage
 Registration
 Upsampling
 Histogram matching
 Image fusion stage

10. The Proposed Image Fusion Approach
Fusion Algorithm
1: Apply the Brovey transform on the multispectral images (R, G, and B) and the panchromatic
image and produce new images (Rnew, Gnew and Bnew).
2: Decompose the high resolution image (i.e. Pan image) into into a set of low resolution
with the wavelet transform.
3: The wavelet transform with the same decomposition scale is applied to obtain the
Wavelet coefficients of the new image (Rnew, Gnew and Bnew).
4: Replace a low frequency of Pan image with low frequency of MS band at the level.
5:The Proposed wavelet coefficients fusion scheme is carried to reconstruct new image’s
wavelet
coefficients.
6: The reconstruct image wavelet coefficients.
7: The last output image is generated by applying inverse wavelet transform (IWT)
with reconstructed wavelet coefficients.

11. Experimental Results
 Various image fusion techniques on MODIS & Spot data
via the proposed technique
MODIS MS Image Spot Panchromatic The Brovey Transform
The IHS Technique The PCA Technique The proposed Technique

12. Experimental Results
 Various image fusion techniques on ETM+ & Spot data via
the proposed technique
Spot Panchromatic
The IHS Technique The PCA Technique The proposed Technique
ETM+ MS Image The Brovey Transform

13. Experimental Results
 Statistical analysis of image fusion techniques using the
six metrics
 The standard deviation (SD)
 The correlation coecient (CC)
 The entropy information (EI)
 The Peak Signal to Noise Ratio (PSNR)
 The structural similarity index (SSIM)

14. Experimental Results
 Statistical analysis of image fusion techniques on MODIS
& Spot data
SSIMPSNRRMSECCEISDImage
0.756734.87221.17780.90436.456723.0724IHS
0.497732.866333.60840.78566.392236.8083PCA
0.761635.210819.58860.89855.241922.1601BT
0.764235.006220.53320.90446.512423.6531proposed

15. Experimental Results
 Statistical analysis of image fusion techniques on ETM+ &
Spot data
SSIMPSNRRMSECCEISDImage
0.10628.13599.90330.62685.430781.819IHS
0.474434.571222.69680.66045.715622.3808PCA
0.535.91616.65270.72316.459322.3662BT
0.381634.54622.85630.79647.078528.5334proposed

16. Conclusion and Future works
 The traditional image fusion techniques have limitation and
do not meet the needs of remote sensing
 Therefore our way is the only hybrid systems.
 Hybrid techniques in pixel level are more efficiency
technique than traditional techniques.
 The proposed image fusion technique has achieved good
results and we will be in the future work on improving it.
 In the future work we will use fused images for the
classification and study of the concerned area.

17. For further questions:
Reham Gharbia
Reham_ghrabia@yahoo.com