Hyper-Temporal Data Based Modulation Transfer Functions Compensation for Geostationary Remote Sensing Satellites

Abstract

Over the past years, the acquisition of hyper-temporal data (HTD) from geostationary orbit remote sensing satellites (GEORSS) has provided numerous new research opportunities. Many factors influence the in-orbit dynamic modulation transfer function (MTF) of GEORSS, making it difficult to satisfy the requirements for space-borne cameras. The MTF compensation (MTFC) technique can effectively optimize the design of dynamic MTFs for GEORSS. The traditional MTFC methods mainly consider the sensor, atmosphere and relative motion of the satellite platform when improving GEORSS image quality. They will introduce new high frequency noise, resulting in image information loss. In this paper, a mixed sparse higher-order non-convex total variation (MS-HONCTV) model-aided MTFC method is proposed. By introducing the group sparse regularization (GSR) term into the MS-HONCTV model, it increases the robustness to noise and hence reduces the degeneration of the MTF produced by satellite’ low pointing stability. The MS-HONCTV model is then applied to solve the problem of image degradation. The quality of remote sensing data is improved by the proposed MTFC and this is achieved without modifying the aperture diameter, focal length, or detector size of the satellite’s optical system. Experimental results show that the proposed MS-HONCTV effectively improves the images’ MTF, SNR, gray mean gradient (GMG) and standard deviation (SD), as evidenced by subjective qualitative analysis and objective quantitative assessments of simulated data, laboratory data, and GF-4 satellite data. Compared with other methods, the SNR of the proposed method is increased by 30%, GMG by 14.21% and SD by 6.3% on average.