Thanks to the excellent feature representation capabilities of neural networks, deep learning-based methods perform far better than traditional methods on target detection tasks such as ship detection. Although various network models have been proposed for SAR ship detection such as DRBox-v1, DRBox-v2, and MSR2N, there are still some problems such as mismatch of feature scale, contradictions between different learning tasks, and unbalanced distribution of positive samples, which have not been mentioned in these studies. In this article, an improved one-stage object detection framework based on RetinaNet and rotatable bounding box (RBox), which is referred as R-RetinaNet, is proposed to solve the above problems. The main improvements of R-RetinaNet as well as the contributions of this article are threefold. First, a scale calibration method is proposed to align the scale distribution of the output backbone feature map with the scale distribution of the targets. Second, a feature fusion network based on task-wise attention feature pyramid network is designed to decouple the feature optimization process of different tasks, which alleviates the conflict between different learning goals. Finally, an adaptive intersection over union (IoU) threshold training method is proposed for RBox-based model to correct the unbalanced distribution of positive samples caused by the fixed IoU threshold on RBox. Experimental results show that our method obtains 13.26%, 9.49%, 8.92%, and 4.55% gains in average precision under an IoU threshold of 0.5 on the public SAR ship detection dataset compared with four state-of-the-art RBox-based methods, respectively.

In synthetic aperture radar (SAR) images, ships are often arbitrary-oriented and densely arranged in complex backgrounds, posing enormous challenges for ship detection. However, most existing methods detect ships with horizontal bounding boxes, which leads to the redundancy of detected regions. Furthermore, the high Intersection-over-Union (IoU) between two horizontal bounding boxes of densely arranged ships can cause missing detection. In this paper, a multi-stage rotational region based network (MSR2N) is proposed to solve the above problems. In MSR2N, the rotated bounding boxes, which can reduce background noise and prevent missing detection caused by high IoUs, are utilized to represent ship regions. MSR2N consists of three modules: feature pyramid network (FPN), rotational region proposal network (RRPN), and multi-stage rotational detection network (MSRDN). First of all, the FPN is applied to combine high-resolution features with semantically strong features. Second, in RRPN, a rotation-angle-dependent strategy is employed to generate multi-angle anchors which can represent arbitrary-oriented ship regions more felicitously than horizontal anchors. Finally, the MSRDN with three sub-networks is proposed to regress proposals of ship regions stage by stage. Meanwhile, the incrementally increasing IoU thresholds are selected for resampling positive and negative proposals in sequential stages of MSRDN, which eliminates close false positive proposals successively. With the above characteristics, MSR2N is more suitable and robust for ship detection in SAR images. The experimental results on SAR ship detection dataset (SSDD) show that the MSR2N has achieved state-of-the-art performance.

Synthetic aperture radar (SAR) ship detection is an important part of remote sensing applications. With the development of computer vision, SAR ship detection methods based on convolutional neural network (CNN) can directly perform end-to-end detection of near-shore ship targets. However, CNN-based methods are prone to generate false targets on land areas, especially when using a rotatable bounding box (RBox) for detection. Therefore, how to reduce the false alarm rate becomes a key direction in research for SAR ship detection. In this letter, the problem of negative sample intraclass imbalance in the training stage of CNN-based detection methods is pointed out for the first time, which is considered to be an important reason for the excessive false alarm rate in the land area. Then, a method is proposed to reduce the false targets generated in the land area by CNN-based detection methods. First, an RBox-based model is proposed as the basic architecture for detection. Then, a new loss function is adopted to guide the model to balance the loss contribution of different negative samples during the training stage. The experimental results prove that the proposed method can effectively reduce the false alarm rate of the model and boost the performance of CNN-based detection methods.

Nonuniformly-rotating ship refocusing is very significant in the marine surveillance of satellite synthetic aperture radar (SAR). The majority of ship imaging algorithms is based on the inverse SAR (ISAR) technique. On the basis of the ISAR technique, several parameter estimation algorithms were proposed for nonuniformly rotating ships. But these algorithms still have problems on cross-terms and noise suppression. In this paper, a refocusing algorithm for nonuniformly rotating ships based on the bilinear extended fractional Fourier transform (BEFRFT) is proposed. The ship signal in a range bin can be modeled as a multicomponent cubic phase signal (CPS) after motion compensation. BEFRFT is a bilinear extension of fractional Fourier transform (FRFT), which can estimate the chirp rates and quadratic chirp rates of CPSs. Furthermore, BEFRFT has excellent performances on cross-terms and noise suppression. The results of simulated data and Gaofen-3 data verify the effectiveness of BEFRFT.