Ship detection from synthetic aperture radar (SAR) images has become a major research field in recent years. It plays a major role in monitoring the ocean, marine rescue activities, and marine safety warnings. However, there are still some factors that restrict further improvements in detecting performance, e.g., multi-scale ship transformation and unfocused images caused by motion. In order to resolve these issues, in this paper, a doppler feature matrix fused with a multi-layer feature pyramid network (D-MFPN) is proposed for SAR ship detection. The D-MFPN takes single-look complex image data as input and consists of two branches: the image branch designs a multi-layer feature pyramid network to enhance the positioning capacity for large ships combined with an attention module to refine the feature map’s expressiveness, and the doppler branch aims to build a feature matrix that characterizes the ship’s motion state by estimating the doppler center frequency and frequency modulation rate offset. To confirm the validity of each branch, individual ablation experiments are conducted. The experimental results on the Gaofen-3 satellite ship dataset illustrate the D-MFPN’s optimal performance in defocused ship detection tasks compared with six other competitive convolutional neural network (CNN)-based SAR ship detectors. Its satisfactory results demonstrate the application value of the deep-learning model fused with doppler features in the field of SAR ship detection.

There are two useful methods of current measurement based on synthetic aperture radar (SAR): one is along-track interferometry (ATI), and the other is Doppler centroid analysis (DCA). For the ATI method, the interferometric phase must be accurate enough for ocean current measurements. Therefore, the space-varying of phase imbalances along the range, caused by antenna phase center position error, attitude error, antenna electronic miss pointing, antenna pattern mismatch, and other reasons, cannot be ignored. Firstly, this paper mainly analyzes the above possible factors by using real GF-3 ATI data and error model simulation results. Secondly, the ocean current has been preliminarily measured by the ATI method and the DCA method, using CDOP model, based on the GF-3 ATI data of the ocean scene near Qingdao, China, which is up to around −1.45 m/s. The results of the two methods are in good agreement with the correlation coefficient of 0.98, the mean difference of −0.010 m/s, and the root mean squared error (RMSE) of 0.062 m/s. Moreover, by comparing with the current measured by high-frequency surface wave radar (HFSWR), the correctness of the analysis is further proved.

The primary payload onboard the Gaofen-3-02 (GF3-02) satellite is a C-band Synthetic Aperture Radar (SAR) capable of achieving a maximum resolution of 1 m. This instrument is critical to monitor the marine environment, particularly for tracking sea surface wind speeds, an important marine environmental parameter. In this study, we utilized 192 sets of GF3-02 SAR data, acquired in Quad-Polarization Strip I (QPSI) mode in March 2022, to retrieve sea surface wind speeds. Prior to wind speed retrieval for vertical-vertical (VV) polarization, radiometric calibration accuracy was analyzed, yielding good performance. The results showed a bias and root mean square errors (RMSEs) of 0.02 m/s and 1.36 m/s, respectively, when compared to the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5) data. For horizontal–horizontal (HH) polarization, two types of polarization ratio (PR) models were introduced based on the GF3-02 SAR data. Combining these refitted PR models with CMOD5.N, the results for HH polarization exhibited a bias of −0.18 m/s and an RMSE of 1.25 m/s in comparison to the ERA5 data. Regarding vertical–horizontal (VH) polarization, two linear models based on both measured normalized radar cross sections (NRCSs) and denoised NRCSs were developed. The findings indicate that denoising significantly enhances the accuracy of wind speed measurements for VH polarization when dealing with low wind speeds. When compared against buoy data, the wind speed retrieval results demonstrated a bias of 0.23 m/s and an RMSE of 1.77 m/s. Finally, a comparative analysis of the above retrieval results across all three polarizations was conducted to further understand their respective performances.

The quantization process of synthetic aperture radar (SAR) images faces significant challenges due to their high dynamic range, resulting in notable quantization distortion. This not only degrades the visual quality of the quantized images but also severely impacts the accuracy of image interpretation. To mitigate the distortion caused by uniform quantization and enhance visual quality, this paper introduced a novel nonlinear quantization framework via signal-to-noise ratio (SNR) enhancement and segmentation strategy guidance. This framework introduces guiding information to improve quantization performance in weak scattering regions. A histogram adjustment method is developed to incorporate the spatial information of SAR images into the quantization process to enhance the quantization performance, specifically within weak scattering regions. Additionally, the optimal quantizer is improved by refining the SNR distribution across quantization units, addressing imbalances in their allocation. Experimental results based on Gaofen-3 (GF-3) satellite data demonstrate that the proposed algorithm approaches the global quantization performance of optimal quantizers while achieving superior local quantization performance compared to existing methods.

On 23 November 2021, the Gaofen-3-02 (GF3-02) satellite was successfully launched in the Jiuquan Satellite Launch Center of China. The primary payload is C-band Synthetic Aperture Radar (SAR), with a maximum resolution of 1 m, and includes 12 imaging modes such as Spotlight, Strip, and TOPSAR, which will play an essential role in marine environment monitoring. As an important marine environmental parameter, the wind speed accuracy retrieved by GF3-02 SAR directly reflects its performance and effectiveness as an operational product. Therefore, based on the wind data of buoys of the National Data Buoy Center (NDBC), ECMWF reanalysis V5 (ERA5), and HY-2B Scatterometer (SCA), a preliminary accuracy assessment of the wind speed retrieved by GF3-02 SAR is carried out in this paper. The wind speed retrieval accuracy of GF3-02 SAR in the co-polarization (HH+VV) data under different Geophysical Model Functions (GMFs) is discussed by using 478 level-1A Single Look Complex (SLC) ocean products acquired in Quad-Polarization Strip I (QPSI) and produced by the National Satellite Ocean Application Service (NSOAS) from January to March 2022. The results show that the optimal root mean square errors (RMSE) are 1.40 m/s, 1.18 m/s, and 1.24 m/s for the VV polarization and 1.39 m/s, 1.19 m/s, and 1.52 m/s for the HH polarization compared to the NDBC wind speed, the ERA5 wind speed, and the HY-2B SCA wind speed, respectively. The preliminary results show that GF3-02 SAR has good wind speed retrieval ability and can meet the needs of operational products.

High-resolution wind vector is important to investigate local winds’ variability over the global ocean. Quad-polarization Synthetic Aperture Radar (SAR) can provide wind vector independently without any external wind direction inputs. Although quad-polarization SAR wind retrieval algorithms have been widely studied, improvements are still required. The amplitude and phase imbalance of polarization channel cannot be neglected for improving the wind vector retrieval precision. In this study, rainforest was performed to remove the amplitude and phase imbalance of polarization channel of GF-3 SAR. To explore the applicability of this method for sea surface measurement, the influence of residual amplitude and phase error for GF-3 quad-polarization SAR on wind vector retrieval was assessed. Variation of amplitude and phase imbalance of sea surface for transmit and receive channel were assessed against collocated wind speed and incidence angle. Considering the polarization difference of VV channel relative to HH channel, the residual amplitude and phase error was found to be closely related to wind speed and polarization isolation. Correction of residual amplitude and phase error were employed to improve the retrieval precision of wind vector. It is revealed that the wind speed retrieval precision of VV polarization improved with correction of residual amplitude error. In addition, the influence of residual amplitude and phase error on wind direction retrieval can be neglected. Thus, it is concluded that correction of amplitude and phase error has the potential to improve wind vector retrievals from GF-3 quad-polarization SAR.