Gaofen-3 is China’s first meter-level multipolarization synthetic aperture radar (SAR) satellite with 12 imaging modes for the scientific and commercial applications. In order to evaluate the imaging performance of these modes, the multiple mode SAR raw data simulation is highly demanded. In the paper, the multiple mode SAR simulation framework will be briefly introduced to expose how the raw data simulation guarantees the development of Gaofen-3 and its ground processing system. As an engineering simulation, the complex working modes and practical evaluation requirements of Gaofen-3 mission put forward to the higher demand for simulation simplification and data input/output (I/O) efficiency. To meet the requirements, two improvements have been proposed. First, the stripmap mode based multiple mode decomposition method is introduced to make a solid and simplified system simulation structure. Second, the cloud computing and graphics processing unit (GPU) are integrated to simulate the practical huge volume raw data, resulting in improved calculation and data I/O efficiency. The experimental results of sliding spotlight imaging prove the effectiveness of the Gaofen-3 mission simulation framework and the decomposition idea. The results for efficiency assessment show that the GPU cloud method greatly improves the computing power of a 16-core CPU parallel method about $40\times$ speedup and the data throughput with the Hadoop distributed file system. These results prove that the simulation system has the merits of coping with multiple modes and huge volume raw data simulation and can be extended to the future space-borne SAR simulation.

Clouds are one of the most serious disturbances when using satellite imagery for ground observations. The semi-translucent nature of thin clouds provides the possibility of 2D ground scene reconstruction based on a single satellite image. In this paper, we propose an effective framework for thin cloud removal involving two aspects: a network architecture and a training strategy. For the network architecture, a Wasserstein generative adversarial network (WGAN) in YUV color space called YUV-GAN is proposed. Unlike most existing approaches in RGB color space, our method performs end-to-end thin cloud removal by learning luminance and chroma components independently, which is efficient at reducing the number of unrecoverable bright and dark pixels. To preserve more detailed features, the generator adopts a residual encoding–decoding network without down-sampling and up-sampling layers, which effectively competes with a residual discriminator, encouraging the accuracy of scene identification. For the training strategy, a transfer-learning-based method was applied. Instead of using either simulated or scarce real data to train the deep network, adequate simulated pairs were used to train the YUV-GAN at first. Then, pre-trained convolutional layers were optimized by real pairs to encourage the applicability of the model to real cloudy images. Qualitative and quantitative results on RICE1 and Sentinel-2A datasets confirmed that our YUV-GAN achieved state-of-the-art performance compared with other approaches. Additionally, our method combining the YUV-GAN with a transfer-learning-based training strategy led to better performance in the case of scarce training data.

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.

The distribution of sea ice is one of the major safety hazards for sea navigation. As human activities in polar regions become more frequent, monitoring and forecasting of sea ice are of great significance. In this paper, we use SAR data from the C-band synthetic aperture radar (SAR) Gaofen-3 satellite in the dual-polarization (VV, VH) fine strip II (FSII) mode of operation to study the Arctic sea ice classification in winter. SAR data we use were taken in the western Arctic Ocean from January to February 2020. We classify the sea ice into four categories, namely new ice (NI), thin first-year ice (tI), thick first-year ice (TI), and old ice (OI), by referring to the ice maps provided by the Canadian Ice Service (CIS). Then, we use the deep learning model MobileNetV3 as the backbone network, input samples of different sizes, and combine the backbone network with multiscale feature fusion methods to build a deep learning model called Multiscale MobileNet (MSMN). Dual-polarization SAR data are used to synthesize pseudocolor images and produce samples of sizes 16 × 16 × 3, 32 × 32 × 3, and 64 × 64 × 3 as input. Ultimately, MSMN can reach over 95% classification accuracy on testing SAR sea ice images. The classification results using only VV polarization or VH polarization data are tested, and it is found that using dual-polarization data could improve the classification accuracy by 10.05% and 9.35%, respectively. When other classification models are trained using the training data from this paper for comparison, the accuracy of MSMN is 4.86% and 1.84% higher on average than that of the model built using convolutional neural networks (CNNs) and ResNet18 model, respectively.

Infrared imaging plays an important role in space-based early warning and anti-missile guidance due to its particular imaging mechanism. However, the signal-to-noise ratio of the infrared image is usually low and the target is moving, which makes most of the existing methods perform inferiorly, especially in very complex scenes. To solve these difficulties, this paper proposes a novel multi-frame spatial–temporal patch-tensor (MFSTPT) model for infrared dim and small target detection from complex scenes. First, the method of simultaneous sampling in spatial and temporal domains is adopted to make full use of the information between multi-frame images, establishing an image-patch tensor model that makes the complex background more in line with the low-rank assumption. Secondly, we propose utilizing the Laplace method to approximate the rank of the tensor, which is more accurate. Third, to suppress strong interference and sparse noise, a prior weighted saliency map is established through a weighted local structure tensor, and different weights are assigned to the target and background. Using an alternating direction method of multipliers (ADMM) to solve the model, we can accurately separate the background and target components and acquire the detection results. Through qualitative and quantitative analysis, experimental results of multiple real sequences verify the rationality and effectiveness of the proposed algorithm.

In recent years, synthetic aperture radar (SAR) automatic target recognition (ATR) has been widely used in both military and civilian fields. Due to the sensitivity of SAR images to the observation azimuth, the multi-aspect SAR image sequence contains more information for recognition than a single-aspect one. Nowadays, multi-aspect SAR target recognition methods mainly use recurrent neural networks (RNN), which rely on the order between images and thus suffer from information loss. At the same time, the training of the deep learning model also requires a lot of training data, but multi-aspect SAR images are expensive to obtain. Therefore, this paper proposes a multi-aspect SAR recognition method based on self-attention, which is used to find the correlation between the semantic information of images. Simultaneously, in order to improve the anti-noise ability of the proposed method and reduce the dependence on a large amount of data, the convolutional autoencoder (CAE) used to pretrain the feature extraction part of the method is designed. The experimental results using the MSTAR dataset show that the proposed multi-aspect SAR target recognition method is superior in various working conditions, performs well with few samples and also has a strong ability of anti-noise.