The newly launched Fengyun-3D (FY-3D) satellite carried a wide-field auroral imager (WAI) that was developed by Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), which will provide a large field of view (FOV), high spatial resolution, and broadband ultraviolet images of the aurora and the ionosphere by imaging the N₂ LBH bands of emissions. The WAI consists of two identical cameras, each with an FOV of 68° in the along-track direction and 10° in the cross-track direction. The two cameras are tilted relative to each other to cover a fan-shaped field of size 130° × 10°. Each camera consists of an unobstructed four-mirror anastigmatic optical system, a BaF₂ filter, and a photon-counting imaging detector. The spatial resolution of WAI is ~10 km at the nadir point at a reference height of 110 km above the Earth’s surface. The sensitivity is >0.01 counts s⁻¹ Rayleigh⁻¹ pixel⁻¹ (140–180 nm) for both cameras, which is sufficient for mapping the boundaries and the fine structures of the auroral oval during storms/substorms. Based on the tests and calibrations that were conducted prior to launch, the data processing algorithm includes photon signal decoding, geometric distortion correction, photometric correction, flat-field correction, line-of-sight projection and correction, and normalization between the two cameras. Preliminarily processed images are compared with DMSP SSUSI images. The agreement between the images that were captured by two instruments demonstrates that the WAI and the data processing algorithm operate normally and can provide high-quality scientific data for future studies on auroral dynamics.

The solar X-ray and Extreme Ultraviolet Imager (X-EUVI), developed by the Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (CIOMP), is the first space-based solar X-ray and Extreme ultraviolet (EUV) imager of China loaded on the Fengyun-3E (FY-3E) satellite supported by the China Meteorological Administration (CMA) for solar observation. Since started work on July 11, 2021, X-EUVI has obtained many solar images. The instrument employs an innovative dual-band design to monitor a much larger temperature range on the Sun, which covers 0.6–8.0 nm in the X-ray region with six channels and 19.5 nm in the EUV region. X-EUVI has a field of view of 42′, an angular resolution of 2.5″ per pixel in the EUV band and an angular resolution of 4.1″ per pixel in the X-ray band. The instrument also includes an X-ray and EUV irradiance sensor (X-EUVS) with the same bands as its imaging optics, which measures the solar irradiance and regularly calibrates the solar images. The radiometric calibration of X-EUVS on the ground has been completed, with a calibration accuracy of 12%. X-EUVI is loaded on the FY-3E satellite and rotates relative to the Sun at a uniform rate. Flat-field calibration is conducted by utilizing successive rotation solar images. The agreement between preliminarily processed X-EUVI images and SDO/AIA and Hinode/XRT images indicates that X-EUVI and the data processing algorithm operate properly and that the data from X-EUVI can be applied to the space weather forecast system of CMA and scientific investigations on solar activity.

One of the challenges in the field of remote sensing is how to automatically identify and classify high-resolution remote sensing images. A number of approaches have been proposed. Among them, the methods based on low-level visual features and middle-level visual features have limitations. Therefore, this paper adopts the method of deep learning to classify scenes of high-resolution remote sensing images to learn semantic information. Most of the existing methods of convolutional neural networks are based on the existing model using transfer learning, while there are relatively few articles about designing of new convolutional neural networks based on the existing high-resolution remote sensing image datasets. In this context, this paper proposes a multi-view scaling strategy, a new convolutional neural network based on residual blocks and fusing strategy of pooling layer maps, and uses optimization methods to make the convolutional neural network named RFPNet more robust. Experiments on two benchmark remote sensing image datasets have been conducted. On the UC Merced dataset, the test accuracy, precision, recall, and F1-score all exceed 93%. On the SIRI-WHU dataset, the test accuracy, precision, recall, and F1-score all exceed 91%. Compared with the existing methods, such as the most traditional methods and some deep learning methods for scene classification of high-resolution remote sensing images, the proposed method has higher accuracy and robustness.

The solar X-ray and Extreme Ultraviolet Imager (X-EUVI), which was developed by CIOMP, is China’s first space-based solar X-ray and Extreme Ultraviolet (EUV) imager; it has been loaded onto the Fengyun-3E Satellite, which is supported by the China Meteorological Administration (CMA), for solar observation. It commenced working on July 11, 2021, and was used to obtain the first X-ray and EUV images in China. X-EUVI employs an innovation dual band design to monitor a much larger temperature range across the Sun, covering the 0.6–8.0 nm wavelength band of the X-ray region and the 19.5 nm band of the EUV region.

Extreme ultraviolet (EUV) observations are widely used in solar activity research and space weather forecasting since they can observe both the solar eruptions and the source regions of the solar wind. Flat field processing is indispensable to remove the instrumental non-uniformity of a solar EUV imager in producing high-quality scientific data from original observed data. FengYun-3E (FY-3E) is a meteorological satellite operated in a Sun-synchronous orbit, and the routine EUV imaging data from the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) onboard FY-3E has the characteristic of concentric rotation. Taking advantage of the concentric rotation, we propose a post-hoc flat field measurement method for its EUV 195 Å channel in this paper. This method removes the small-scale and time-varying component of coronal activities by taking the median value for each pixel along the time axis of a concentric rotation data cube, and then derives the large-scale and invariable component of the quiet coronal radiation, and finally generates a flat field image. The flat field can be generated with cadences from hundreds of minutes (one orbit) to several days. Higher flat field accuracy can be achieved by employing more data. Further analysis shows that our method is able to measure the instrumental spot-like non-uniformity possibly caused by contamination on the detector, which mostly disappears after the in-orbit self-cleaning process. It can also measure the quasi-periodic grid-like non-uniformity, possibly from the obscuration of the support mesh on the rear filter. After flat field correction, these instrumental non-uniformities from the original data are effectively removed. Moreover, the X-EUVI 195 Å data after dark and flat field corrections are consistent with the 193 Å imaging data from the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory, verifying the suitability of the method. The post-hoc method does not occupy observation time, which is advantageous for space weather operations. Our method is not only suitable for FY-3E/X-EUVI but also a candidate method for the flat field measurement of future solar EUV telescopes.