Pansharpening and spatiotemporal image fusion method for remote sensing

In last decades, remote sensing technology has rapidly progressed, leading to the development of numerous earth satellites such as Landsat 7, QuickBird, SPOT, Sentinel-2, and IKONOS. These satellites provide multispectral images with a lower spatial resolution and panchromatic images with a higher spatial resolution. However, satellite sensors are unable to capture images with high spatial and spectral resolutions simultaneously due to storage and bandwidth constraints, among other things. Image fusion in remote sensing has emerged as a powerful tool for improving image quality and integrating important features from multiple source images into one, all while maintaining the integrity of critical features. It is especially useful for high-resolution remote sensing applications that need to integrate features from multiple sources and hence a vital pre-processing step for various applications, including medical, computer vision, and satellite imaging. This review initially gives a basic framework for image fusion, followed by statistical analysis and a comprehensive review of various state-of-the-art image fusion methods, where they are classified based on the number of sensors used, processing levels, and type of information being fused. Subsequently, a thorough analysis of STF and pansharpening techniques for remote sensing applications has been covered, where the dataset of the DEIMOS-2 satellite is employed for evaluating various pansharpening methods while MODIS and Landsat images are employed in the spatiotemporal fusion method. A comparative evaluation of several approaches has been carried out to assess the merits and drawbacks of the current approaches. Several real-time applications of remote sensing image fusion have been explored, and current and future directions in fusion research for remote sensing have been discussed, along with the obstacles they present.