Investigation of Image Fusion for Remote Sensing Application

Remote sensing techniques have proven to be powerful tools for the monitoring of the Earth’s surface and atmosphere on a global, regional, and even local scale, by providing im‐ portant coverage, mapping and classification of land cover features such as vegetation, soil, water and forests. The volume of remote sensing images continues to grow at an enormous rate due to advances in sensor technology for both high spatial and temporal resolution sys‐ tems. Consequently, an increasing quantity of image data from airborne/satellite sensors have been available, including multi-resolution images, multi-temporal images, multi-fre‐ quency/spectral bands images and multi-polarization image. Remote sensing information is convenient and easy to be accessed over a large area at low cost, but due to the impact of cloud, aerosol, solar elevation angle and bio-directional reflection, the surface energy pa‐ rameters retrieved from remote sensing data are often missing; meanwhile, the seasonal var‐ iation of surface parameter time-series plots will be also affected. To reduce such impacts, generally time composite method is adopted. The goal of multiple sensor data fusion is to integrate complementary and redundant information to provide a composite image which could be used to better understanding of the entire scene.