In recent years, lunar exploration has become a hot spot in the world again. High-resolution lunar surface images are of great significance to lunar research, and at the same time are crucial to the safe landing of lunar probes. Due to the limitation of the orbital height and hardware, the resolution of the lunar remote sensing images is restricted, so it is particularly important to carry out super-resolution reconstruction of the lunar surface image. At present, most image super-resolution algorithms use a single fixed degradation model, such as using only bicubic interpolation algorithm for down-sampling, or adding specified blur, noise, etc. However, the real image degradation model is extremely complex and difficult to express with specific formulas, so this paper introduces a more complex degradation model when super-resolving the lunar image and simulates the complex degradation process in reality by adding more randomness. Secondly, this paper uses a deep learning network that combines a CNN network with residual structure and a transformer architecture for image super-resolution reconstruction, where the transformer architecture is used for deep feature extraction. The proposed method is experimented on Chang'e-2 7-meter resolution lunar surface remote sensing images, which verifies the effectiveness of the super-resolution algorithm proposed in this paper and outperforms the current popular methods in terms of visual effects and commonly used evaluation metrics. This work aims to improve the image clarity of the lunar surface in order to enhance the environment-awareness capability of the lunar probe and further improve its autonomous capability on the lunar surface.
This high-resolution satellite is equipped with a push-broom high-resolution camera (PMS) that consists of panchromatic, blue, green, red, and near-infrared bands. The Moon is considered an stable light source, unaffected by atmospheric conditions, making it an ideal reference for absolute and relative radiation calibration of remote sensors. To utilize the Moon for calibration purposes, the satellite implemented two specific imaging modes: lunar push broom for absolute radiation calibration and lunar side-slither for relative radiation calibration. The lunar observations conducted by this satellite in orbit were highly successful. In 2019, the satellite conducted lunar observations at various lunar phase angles, while in 2021 and 2022, observations were specifically conducted during the full moon. These observations yielded many effective full lunar disk images. The stability of the PMS camera was analyzed using the band ratio irradiance method. Analysis of the satellite's four-year lunar observation data revealed a strong correlation between the lunar irradiance measured by PMS and the lunar phase angle. The analysis of the band ratio indicated that the multi-spectral bands are stable. However, the PAN band exhibited a tendency to attenuate, with a decay rate of approximately 0.0086 per year.
This optical remote sensing satellite has a high-resolution PMS camera, which has a panchromatic band (PAN) and four multispectral bands (MS). The PMS camera do not contain onboard calibration subsystem. So, the satellite is designed to allow a three-axis attitude control system pointing the camera to the lunar. Through the lunar imaging, the cameras’ long-term stability of the radiances is hope to be evaluated. The lunar is known as an excellent radiometric reference because of the stable reflectance of its surface in the visible and near-infrared spectral regions. It is the nearest planet to the earth. In orbit lunar imaging does not rely on ground calibration site and weather. Although earth-orbit satellite lunar observation is efficient to monitoring camera’s radiation performance or achieving absolute radiation calibration, attitude control and imaging parameters matching problem should be solved. Some key points of lunar imaging were proposed to solve the mismatch of integration time and push-broom velocity. In addition, this paper summarizes the improved scheme for lunar imaging, obtains clear images at different lunar phase angles, and shows the preliminary results of the geometric parameters and irradiance of the lunar observation. It has accumulated experience in the follow-up satellite lunar observation.
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