Data compression of multispectral images for FY-2C geostationary meteorological satellite

Abstract

FY-2C is geostationary satellite which is researched and developed by China. The primary advantage of geostationary satellite is the ability to characterize the radiance by obtaining numerous views of a specific earth location at any time of a day. This allows the production of a composite image to monitor short-term weather better. In this paper, data compression from the composite multi-spectral multispectral images of FY-2C has been described, which shows considerable promise in the detection of fog and cloud for aviation and marine weather forecasting. By applying Karhunen-Loeve transform to raw data of FY-2C, the infrared images are analyzed. By comparing Eigen image of these infrared images with visible image in the same batch, it is concluded that data of IR3 contribute to the first Eigen image mostly, which shows that the newly added IR3 channel of FY-2C has greatly improved the ability of distinguishing short time weather phenomena. The state-of-the-art image compression techniques can exploit the dependencies between the subbands in a wavelet transformed image. In this paper, by applying Wavelet Transform for multispectral images, the spatial resolutions of images are enhanced, edge and feature extraction are realized.