The Gaofen-3 (GF-3) satellite is Chinas first civilian quad-polarized C-band imaging microwave satellite that can provide high-resolution marine and land observations. Also, it is China's first low earth orbit remote sensing satellite with a design life of 8 years. During the design and development of this satellite, many major challenges have been overcome, such as the design of complex RF channels, difficult system integration, high quantitative requirements, and strict component life requirements. In this paper, a series of independent technological innovations in the development of GF-3 is presented. Meanwhile, the on-orbit operation of the satellite in the past five years is introduced, as well as the application of satellite data in marine observation, emergency relief, disaster reduction, surveying and mapping, water conservancy, etc. The further advancement and improvement of the GF-3 satellite are described at the end of this paper.

Target classification techniques using spectral imagery and light detection and ranging (LiDAR) are widely used in many disciplines. However, none of the existing methods can directly capture spectral and 3D spatial information simultaneously. Multispectral LiDAR was proposed to solve this problem as its data combines spectral and 3D spatial information. Point-based classification experiments have been conducted with the use of multispectral LiDAR; however, the low signal to noise ratio creates salt and pepper noise in the spectral-only classification, thus lowering overall classification accuracy. In our study, a two-step classification approach is proposed to eliminate this noise during target classification: routine classification based on spectral information using spectral reflectance or a vegetation index, followed by neighborhood spatial reclassification. In an experiment, a point cloud was first classified with a routine classifier using spectral information and then reclassified with the k-nearest neighbors (k-NN) algorithm using neighborhood spatial information. Next, a vegetation index (VI) was introduced for the classification of healthy and withered leaves. Experimental results show that our proposed two-step classification method is feasible if the first spectral classification accuracy is reasonable. After the reclassification based on the k-NN algorithm was combined with neighborhood spatial information, accuracies increased by 1.50–11.06%. Regarding identification of withered leaves, VI performed much better than raw spectral reflectance, with producer accuracy increasing from 23.272% to 70.507%.

The purpose of our work is to determine the feasibility and effectiveness of retrieving sea surface wind speeds from C-band cross-polarization (herein vertical-horizontal, VH) Chinese Gaofen-3 (GF-3) SAR images in typhoons. In this study, we have collected three GF-3 SAR images acquired in Global Observation (GLO) and Wide ScanSAR (WSC) mode during the summer of 2017 from the China Sea, which includes the typhoons Noru, Doksuri and Talim. These images were collocated with wind simulations at 0.12° grids from a numeric model, called the Regional Assimilation and Prediction System-Typhoon model (GRAPES-TYM). Recent research shows that GRAPES-TYM has a good performance for typhoon simulation in the China Sea. Based on the dataset, the dependence of wind speed and of radar incidence angle on normalized radar cross (NRCS) of VH-polarization GF-3 SAR have been investigated, after which an empirical algorithm for wind speed retrieval from VH-polarization GF-3 SAR was tuned. An additional four VH-polarization GF-3 SAR images in three typhoons, Noru, Hato and Talim, were investigated in order to validate the proposed algorithm. SAR-derived winds were compared with measurements from Windsat winds at 0.25° grids with wind speeds up to 40 m/s, showing a 5.5 m/s root mean square error (RMSE) of wind speed and an improved RMSE of 5.1 m/s wind speed was achieved compared with the retrieval results validated against GRAPES-TYM winds. It is concluded that the proposed algorithm is a promising potential technique for strong wind retrieval from cross-polarization GF-3 SAR images without encountering a signal saturation problem.

Synthetic aperture radar (SAR) has played a crucial role in ship monitoring, maritime rescue, and other fields due to its ability to operate around the clock and in all weather conditions. The interpretation and analysis of ship targets in SAR images have always been an important research direction in the field of remote sensing. In recent years, with the application of deep learning technology to the field of SAR image interpretation, an increasing number of deep-learning-based ship interpretation methods have been proposed and have achieved many new results in various tasks such as ship detection, identification, parameter estimation, and others. Against this backdrop, it is necessary to review the recently proposed deep-learning-based SAR image ship interpretation technologies. This paper reviews English journal and conference articles published from January 2022 to May 2025. First, we provide a detailed organization and statistical analysis of the commonly used and newly released public datasets in this field. Then, focusing on the ship detection and identification task, which has the highest research interest and the largest number of research papers, we innovatively categorize the methods into two classes based on whether they consider the characteristics of SAR image: deep-learning-based methods directly transferred from optical and the methods specifically designed for the characteristics of SAR images. Subsequently, we sorts out and summarize the new tasks and challenges proposed in the field of SAR image ship interpretation, such as cross-domain SAR image ship detection and recognition methods, ship detection with incomplete SAR imaging data, ship wake detection, ship parameter estimation, and three-dimensional ship refocusing. Finally, we summarize the current research status of this field and speculates on possible future research directions.

As a crucial sensor for wide-area Earth observation, spaceborne synthetic aperture radar (SAR) plays a pivotal role in large-scale terrain mapping, ocean observation, disaster monitoring, and so forth. Driven by the increasing demands for diverse applications, enhanced performance, and the continuous advancement of satellite and radar technologies, the distributed configuration has emerged as a key developmental trend for spaceborne SAR. This review comprehensively summarizes the systems and typical applications of distributed spaceborne SAR. The system configurations encompass homogenous distributed SAR, formed by multiple identical or similar platforms, and heterogeneous distributed SAR, characterized by significant differences between the transmitting and receiving platforms. Typical applications of distributed SAR include intelligent target recognition, terrain mapping, deformation retrieval, atmosphere measurement, and ocean observation, among others. Finally, the review offers a prospective outlook on the future development of distributed spaceborne SAR.

A sparse antenna array with flat-top and sharp cut-off radiation patterns is presented in this work. The array consists of sixteen antenna elements, which is realized by applying a symmetrical sparse configuration with uniform excitation phase. This configuration simplifies the excitations of the array while maintaining excellent radiation performance. The dual-band dual-polarized base station antenna based on the proposed sparse antenna arrays in the bands of 1.71 GHz ~ 2.17 GHz and 2.30 GHz ~ 2.69 GHz is fabricated and measured. The measured results are in very good agreement with the predicted ones, showing that the port reflection coefficient is less than -15.0 dB, and the isolation is greater than 28.5 dB. In addition, the array has a gain of 10.1 to 12.3 dB, a half-power beamwidth (HPBW) of 45° ± 5° in both the horizontal and vertical planes, a cut-off transition of less than 17.4° from -3 dB to -20 dB between the main beam and the sidelobe area, and a sidelobe level suppressed to below -21.1 dB. Apart from the good radiation performance, the proposed sparse antenna array has a compact size with very few antenna elements, which is very suitable for communication in a crowded scene.