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INTRODUCTION
Image fusion is an important tool in remote sensing, as many Earth observation satellites provide both high-resolution panchromatic and low-resolution multispectral images Most Earth observation satellites, such as SPOT, IRS, Landsat 7, IKONOS, and QuickBird, provide both
panchromatic images at a higher spatial resolution and multispectral images at a lower spatial resolution. We have to fuse these two images to produce a high resolution colour image.
Satellites often capture two different types of images: multispectral and panchromatic. A multispectral image (MS) is a four-band image (R, G, B, IR) which has low spatial resolution but accurate color data. A panchromatic image is a grayscale image which has higher spatial resolution.
Pan-sharpening combines a grayscale PAN image with a MS image to obtain a sharper fused color image. There are two aspects of the fused image that need to be analyzed and compared: spatial quality, spectral quality. There are many different pan-sharpening methods. Wavelet fusion method, PCA (Principal Component Analysis) fusion, P+XS method, VWP method, IHS colour (Intensity- Hue-Saturation) fusion.
They all experience a tradeoff between the final spatial and spectral resolutions and there is no method which is clearly the best overall. There is also no standard way to judge the fusion results. Our group’s goal is to compare and build on existing pan-sharpening methods and to produce an integrated pan sharpening method and to write the MATLAB code for the same. Among the these categories, the IHS technique has been most widely used in the practical applications, and the wavelet fusion technique has been discussed most frequently in the recent publications due to its advantages over other fusion techniques Therefore, this study focuses on the IHS and the wavelet fusion methods, and explores their potential for further improvement. With the IHS fusion, if the intensity image of the IHS transform has a high correlation to the panchromatic image being fused, it will produce a satisfactory fusion result. The higher the correlation is, the less colour distortion the fused results have.

IMAGE FUSION
Fusing information contained in multiple images plays an increasingly important role for
quality inspection in industrial processes as well as in situation assessment for autonomous
systems and assistance systems. The aim of image fusion in general is to use images as
redundant or complementary sources to extract information from them with higher accuracy or
reliability. Among others, the perhaps most interesting scenario is to use complementary image
information obtained by varying one or several imaging parameters, such as the camera spectral
response, polarization filters, dynamic range or aperture setting. However, the challenges of
image fusion are still numerous: for an inspection task, specific imaging constellations yielding
the desired information must be found. Furthermore, fusion techniques are required to produce a
result that offers the desired accuracy and reliability. In addition, the algorithms must meet the
requirements of affordable calculation time in real-time systems.

PAN-SHARPENING
Pan-sharpening combines a grayscale PAN image with a MS(multi spectral) image to obtain a sharper fused color image. There are two aspects of the fused image that need to be analyzed and compared: spatial quality, spectral quality. Pan sharpening is a pixel level fusion technique used to increase the spatial resolution of the multispectral image. Pan sharpening techniques increase the spatial resolution while simultaneously preserving the spectral information in the multispectral data. Pan sharpening is also known as resolution merge, image integration, and multi sensor data fusion. Some of the applications of pan sharpening include improving geometric correction, enhancing certain features not visible in either of the single data alone, change detection using temporal data sets, and enhancing classification.
3.1 SPATIAL RESOLUTION AND SPECTRAL RESOLUTION
Spatial resolution of an imaging system is expressed as the area of the ground represented by one pixel. The instantaneous field of view (IFOV) is the ground area sensed by the sensor at a given instant in time. The spatial resolution is dependent on the IFOV. The finer the IFOV is, the higher the spatial resolution. Spatial resolution is also viewed as the clarity of the high frequency detail information available in an image. As the spatial resolution increases the details in an image are clear.
Spectral resolution is the width within the electromagnetic spectrum that can be sensed by a band in a sensor. The narrower the spectral bandwidth is, the higher the spectral resolution. If the platform has a few spectral bands, typically 4 to 7, they are called multispectral, and if the number of spectral bands in hundreds, they are called hyper spectral data.

DIFFERENT FUSION METHODS
There are many method to perform pan sharpening, main methods are
1) Wavelet fusion method
2) PCA (Principal Component Analysis) fusion
3) P+XS method
4) VWP method
5) IHS colour (Intensity- Hue-Saturation) fusion