The field of remote sensing is a continuously growing market with applications like vegetation mapping and observation of the environment. The increase in applications is due to the availability of high quality images for a reasonable price and improved computation power. However, as a result of the demand for higher classification accuracy and the need in enhanced positioning precision (e.g. for geoscience information systems) there is always a need to improve the spectral and spatial resolution of remotely sensed imagery. These requirements can be either fulfilled by building new satellites with a superior resolution power, or by the utilization of image processing techniques. The main advantage of the second alternative is the significantly lower expense. Remote sensing images are distinct in terms of imaging as the images are captured over a wide range of the electromagnetic spectrum such as the thermal infrared and the microwave spectrum. Also, interpreting the satellite images requires a previous experience and understanding of the nature of the Earth’s surface and the target areas. Moreover, analysis of remote sensing images aims to extract the information about the land cover and sometimes present it as a graphic format (such as maps).The sensors launched on the satellites provide a variety of images with different specifications. Therefore, it is required to introduce an efficient technique that integrates the output of two or more sensors and produces more accurate images to meet the requirements of remote sensing applications including: environmental change detection agricultural monitoring and urban planning. Therefore, image fusion is a promising research area in the image-based application fields. The aim of the paper is to produce a single image from set of images using Contourlet transform. Considerable progress has been made in the fusion of images from different imaging modalities using software approaches. One goal of fusion software is to align anatomical and functional images and allow improved spatial localization of abnormalities. The software part is using MATLAB for fusing images.
