Deep learning has been widely used in the field of SAR ship detection. However, current SAR ship detection still faces many challenges, such as complex scenes, multiple scales, and small targets. In order to promote the solution to the above problems, this article releases a high-resolution SAR ship detection dataset which can be used for rotating frame target detection. The dataset contains six categories of ships. In total, 30 panoramic SAR tiles of the Chinese Gaofen-3 of port areas with a 1-m resolution were cropped to slices, each with 1024 × 1024 pixels. In addition, most of the images in the dataset contain nearshore areas with complex background interference. Eight state-of-the-art rotated detectors and a CFAR-based method were used to evaluate the dataset. Experimental results revealed that the complex background will have a great impact on the performance of detectors.

In recent years, convolutional neural networks (CNNs) have shown great success in the scene classification of computer vision images. Although these CNNs can achieve excellent classification accuracy, the discriminative ability of feature representations extracted from CNNs is still limited in distinguishing more complex remote sensing images. Therefore, we propose a unified feature fusion framework based on attention mechanism in this paper, which is called Deep Discriminative Representation Learning with Attention Map (DDRL-AM). Firstly, by applying Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, attention maps associated with the predicted results are generated in order to make CNNs focus on the most salient parts of the image. Secondly, a spatial feature transformer (SFT) is designed to extract discriminative features from attention maps. Then an innovative two-channel CNN architecture is proposed by the fusion of features extracted from attention maps and the RGB (red green blue) stream. A new objective function that considers both center and cross-entropy loss are optimized to decrease the influence of inter-class dispersion and within-class variance. In order to show its effectiveness in classifying remote sensing images, the proposed DDRL-AM method is evaluated on four public benchmark datasets. The experimental results demonstrate the competitive scene classification performance of the DDRL-AM approach. Moreover, the visualization of features extracted by the proposed DDRL-AM method can prove that the discriminative ability of features has been increased.

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 light-small multi-rotor unmanned aerial vehicle (UAV) carrying with a mini synthetic aperture radar (SAR) payload has provided an advanced information acquisition technology for the target detecting and imaging fields. In this paper, a W-band compact SAR system is proposed, which has been successfully deployed on the unmanned aerial vehicle (UAV) platform demonstrating state-of-the-art imaging resolution of about 4.5 cm. In order to achieve remarkable compactness, light weight and power-saving feature for the proposed system, the techniques of the three-dimensional (3-D) integration are adopted, where a whole-system size of only $67\times 60 \times 50$ mm ³ and the weight of 400 g are obtained. This proposed system achieves higher SAR-imaging resolution thanks to the contributions of many efforts, including the technique of high-linearity waveform generation, the substrate integrated waveguide (SIW) antenna with high isolation by the electromagnetic band-gap (EBG) mechanism, and a novel motion compensation method for the imaging formation. To validate this proposed idea, a UAV SAR prototype operating at W-band is designed and fabricated. The measurements show great signal-to-noise ratio imaging results with excellent focusing effect. The proposed SAR system is promisingly an ideal candidate for the target detecting/imaging applications deployed on the UAV platform.

Cross-modal remote sensing (RS) image retrieval aims to retrieve RS images using other modalities (e.g., text) and vice versa. The relationship between objects in RS image is complex, i.e., the distribution of multiple types of objects is uneven, which makes the matching with query text inaccurate, and then restricts the performance of remote sensing image retrieval. Previous methods generally focus on the feature matching between RS image and text and rarely model the relationships between features of RS image. Hypergraph (hyperedge connecting multiple vertices) is an extended structure of a regular graph and has attracted extensive attention for its superiority in representing high-order relationships. Inspired by the advantages of the hypergraph, in this work, a Hypergraph-Enhanced Textual-Visual Matching Network (HyperMatch) is proposed to circumvent the inaccurate matching between RS image and query text. Specifically, a multiscale RS image hypergraph network is designed to model the complex relationships between features of the RS image for forming the valuable and redundant features into different hyperedges. In addition, a hypergraph construction and update method for an RS image is designed. For constructing a hypergraph, the features of an RS image running as vertices and cosine similarity is the metric to measure the correlation between them. Vertex and hyperedge attention mechanisms are introduced for the dynamic update of a hypergraph to realize the alternating update of vertices and hyperedges. Quantitative and qualitative experiments on the RSICD and RSITMD datasets verify the effectiveness of the proposed method in cross-modal remote sensing image retrieval.

In this paper a non-parametric model based on Wasserstein CNN is proposed for color correction. It is suitable for large-scale remote sensing image preprocessing from multiple sources under various viewing conditions, including illumination variances, atmosphere disturbances, and sensor and aspect angles. Color correction aims to alter the color palette of an input image to a standard reference which does not suffer from the mentioned disturbances. Most of current methods highly depend on the similarity between the inputs and the references, with respect to both the contents and the conditions, such as illumination and atmosphere condition. Segmentation is usually necessary to alleviate the color leakage effect on the edges. Different from the previous studies, the proposed method matches the color distribution of the input dataset with the references in a probabilistic optimal transportation framework. Multi-scale features are extracted from the intermediate layers of the lightweight CNN model and are utilized to infer the undisturbed distribution. The Wasserstein distance is utilized to calculate the cost function to measure the discrepancy between two color distributions. The advantage of the method is that no registration or segmentation processes are needed, benefiting from the local texture processing potential of the CNN models. Experimental results demonstrate that the proposed method is effective when the input and reference images are of different sources, resolutions, and under different illumination and atmosphere conditions.