Abstract
Gaofen-4 is a geostationary orbit area array imaging satellite. Due to the difficulty of the on-orbit radiometric calibration of area array cameras, there is system noise in the images. This paper analyzes the source of the system noise, constructs a noise model of Gaofen-4, and proposes a practical method to remove the system noise using multiple images. Gaussian filtering is used to remove radiometric characteristics, and the Grubbs criterion is used to remove gradient characteristics, thereby transforming the images into noise images. System noise can be removed using correction coefficients obtained by superimposing multiple noise images. Using a variety of denoising methods to perform contrast experiments, the results show that the proposed method can effectively maintain image edge details and texture information while removing image noise.
Highlights
The non-uniform response of charge-coupled device (CCD) detectors, dark current in CCDs and other factors will cause system noise in remote sensing images
Thirty-eight Gaofen-4 visible light and near-infrared (VNIR) images are selected as experiment data to calculate the correction coefficients, and one of them is selected as test data to check the denoising effect
Gaofen-4 is equipped with an area array complementary metal–oxide semiconductor (CMOS) camera and an HgCdTe camera
Summary
The non-uniform response of charge-coupled device (CCD) detectors, dark current in CCDs and other factors will cause system noise in remote sensing images. Spatial domain methods utilize the correlation and statistical characteristics between pixels to design filters that reduce image noise [3]. Typical methods, such as median filtering and mean filtering, use neighborhood correlation to remove noise. There are two problems with denoising methods that are based on neighborhood correlation. Perona and Malik proposed a Perona-Malik (P-M) equation method and achieved good performance in denoising and edge reservation [7]. Weikert developed it into an anisotropic diffusion equation, further improving the denoising ability [8]. After solving a rational covariance extension problem [11,12], noise can be suppressed by image compression and decompression, which is an important research direction
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