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

Scientific Research Group in Egyptwww.egyptscience.net

Overview

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

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.

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

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.

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.

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

The Proposed Image Fusion Approach

Preprocessing stage Registration Upsampling Histogram matching

Image fusion stage

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 resolutionwith the wavelet transform.

3 :The wavelet transform with the same decomposition scale is applied to obtain theWavelet 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.

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

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

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)

Experimental Results Statistical analysis of image fusion techniques on

MODIS & Spot data

SSIM PSNR RMSE CC EI SD Image

0.7567 34.872 21.1778 0.9043 6.4567 23.0724 IHS

0.4977 32.8663 33.6084 0.7856 6.3922 36.8083 PCA

0.7616 35.2108 19.5886 0.8985 5.2419 22.1601 BT

0.7642 35.0062 20.5332 0.9044 6.5124 23.6531 proposed

Experimental Results Statistical analysis of image fusion techniques on

ETM+ & Spot data

SSIM PSNR RMSE CC EI SD Image

0.106 28.135 99.9033 0.6268 5.4307 81.819 IHS

0.4744 34.5712 22.6968 0.6604 5.7156 22.3808 PCA

0.5 35.916 16.6527 0.7231 6.4593 22.3662 BT

0.3816 34.546 22.8563 0.7964 7.0785 28.5334 proposed

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.

For further questions:Reham Gharbia

Reham_ghrabia@yahoo.com