In this paper, we propose an automatic and efficient method to solve optical and SAR image registration using the improved phase congruency (PC) model. First, evenly-distributed keypoints are extracted from the optical images via the block harris method. Complementary grid points are then selected in image regions with poor structural information and supplemented to the keypoint set. For each keypoint, a robust feature representation that captures the local spatial relationship is proposed based on the improved PC model. Specifically, we propose to use two different PC models, the classic PC and the SAR-PC, to construct features for optical and SAR images respectively. The PC features of several directions are aggregated to construct the feature descriptors, and a similarity metric via the phase correlation of feature descriptors is obtained. The proposed similarity metric can not only find accurate correspondence but also present efficient results without presetting the size of the search region. We compare the proposed method with two baselines and state-of-the-art (SOTA) methods, i.e. OS-SIFT, HOPC, CFOG, in various scenarios. The results show that the proposed method outperforms the baselines and shows comparable performance with SOTA methods in regions with abundant structural information and better performance in regions with less structural information. Moreover, we build a high-resolution optical and SAR image matching dataset, which consists of 10,692 non-overlapping patch-pairs of 256x256 pixels and 1-m resolution. Results of two benchmarks, Siamese deep matching network and conditional Generative Adversarial Networks, show that this dataset is practical and challenging.

The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format.

Sea surface current is a research hotspot in oceanography. Space-borne along-track interferometric synthetic aperture radar (along-track InSAR, ATI) is a promising sensor for measuring high-resolution sea surface current field, and there is no operational system in orbit yet. To support future space-borne ATI systems, based on the ATI experimental mode of the Gaofen-3 (GF-3) satellite, the first sea surface current observing experiment was conducted in the Jiaozhou Gulf in China, in 2019. Meanwhile, SAR observations and in situ instrument measurements of the current are obtained in the experiments. The data is first preprocessed by a processor specially developed for the GF-3 ATI data. Then, the current is extracted based on the M4S mode. The retrieved current of the Jiaozhou Gulf is compared with ground-based high frequency surface wave radar data. The results show that the root mean square error of the surface current observed by the GF-3 satellite is less than 0.2 m/s.

The GaoFen-3 (GF-3) satellite, launched on 10 August 2016, is the first C-band polarimetric synthetic aperture radar (PolSAR) satellite in China. The PolSAR system of GF-3 can collect a significant wealth of information for geophysical research and applications. Being used for related applications, GF-3 PolSAR images must be of good quality. It is necessary to evaluate the quality of polarimetric data and achieve the normalized quality monitoring during 8-year designed life of GF-3. In this study, a new quality assessment method of PolSAR data based on common distributed targets is proposed, and the performance of the method is analyzed by simulations and GF-3 experiments. We evaluate the quality of GF-3 PolSAR data by this method. Results suggest that GF-3 antenna is highly isolated, and the quality of calibrated data satisfies the requests of quantitative applications.

Azimuth multichannel (AMC) synthetic aperture radar (SAR), which contains multiple receiving antennas along the azimuth, can prevent the minimum antenna area constraint and provide high-resolution and wide-swath (HRWS) SAR images. Channel calibration and along-track baseline estimation are important topics in an AMC SAR system, since they have a great impact on image quality. Based on the signal model for stationary target of AMC SAR, this paper first analyses the influence of the along-track baseline and channel imbalances on SAR images by simulation. Then, a novel method to simultaneously estimate the along-track baseline, phase imbalance and range sample time imbalance (RSTI) based on the azimuth cross-correlation in the two-dimensional frequency domain is addressed. In addition, with the help of simulations and real data acquired by Gaofen-3 (GF-3), the effectiveness of this method is verified by comparing with some existing methods. Finally, this paper analyzes the estimation accuracy of this method under different scenarios and signal-to-noise ratios (SNRs), and points out the direction for future research.

Geostationary optical remote sensing satellites, such as the GF-4, have a high temporal resolution and wide coverage, which enables the continuous tracking and observation of ship targets over a large range. However, the ship targets in the images are usually small and dim and the images are easily affected by clouds, islands and other factors, which make it difficult to detect the ship targets. This paper proposes a new method for detecting ships moving on the sea surface using GF-4 satellite images. First, the adaptive nonlinear gray stretch (ANGS) method was used to enhance the image and highlight small and dim ship targets. Second, a multi-scale dual-neighbor difference contrast measure (MDDCM) method was designed to enable detection of the position of the candidate ship target. The shape characteristics of each candidate area were analyzed to remove false ship targets. Finally, the joint probability data association (JPDA) method was used for multi-frame data association and tracking. Our results suggest that the proposed method can effectively detect and track moving ship targets in GF-4 satellite optical remote sensing images, with better detection performance than other classical methods.