This document proposes a remote sensing image fusion approach that combines the Brovey transform and wavelet transforms. The Brovey transform is used first to reduce spectral distortion, followed by a wavelet transform to reduce spatial distortion. The approach was tested on MODIS and SPOT data as well as ETM+ and SPOT data. Statistical analysis showed the proposed technique performed better than traditional fusion techniques like IHS, PCA, and the Brovey transform alone in terms of metrics like correlation coefficient, entropy, and structural similarity. Future work will focus on improving the technique and applying fused images to classification tasks.

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