Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet

Abstract

Ship detection in high-resolution synthetic aperture radar (SAR) imagery is a challenging problem in the case of complex environments, especially inshore and offshore scenes. Nowadays, the existing methods of SAR ship detection mainly use low-resolution representations obtained by classification networks or recover high-resolution representations from low-resolution representations in SAR images. As the representation learning is characterized by low resolution and the huge loss of resolution makes it difficult to obtain accurate prediction results in spatial accuracy; therefore, these networks are not suitable to ship detection of region-level. In this paper, a novel ship detection method based on a high-resolution ship detection network (HR-SDNet) for high-resolution SAR imagery is proposed. The HR-SDNet adopts a novel high-resolution feature pyramid network (HRFPN) to take full advantage of the feature maps of high-resolution and low-resolution convolutions for SAR image ship detection. In this scheme, the HRFPN connects high-to-low resolution subnetworks in parallel and can maintain high resolution. Next, the Soft Non-Maximum Suppression (Soft-NMS) is used to improve the performance of the NMS, thereby improving the detection performance of the dense ships. Then, we introduce the Microsoft Common Objects in Context (COCO) evaluation metrics, which provides not only the higher quality evaluation metrics average precision (AP) for more accurate bounding box regression, but also the evaluation metrics for small, medium and large targets, so as to precisely evaluate the detection performance of our method. Finally, the experimental results on the SAR ship detection dataset (SSDD) and TerraSAR-X high-resolution images reveal that (1) our approach based on the HRFPN has superior detection performance for both inshore and offshore scenes of the high-resolution SAR imagery, which achieves nearly 4.3% performance gains compared to feature pyramid network (FPN) in inshore scenes, thus proving its effectiveness; (2) compared with the existing algorithms, our approach is more accurate and robust for ship detection of high-resolution SAR imagery, especially inshore and offshore scenes; (3) with the Soft-NMS algorithm, our network performs better, which achieves nearly 1% performance gains in terms of AP; (4) the COCO evaluation metrics are effective for SAR image ship detection; (5) the displayed thresholds within a certain range have a significant impact on the robustness of ship detectors.

HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery

“…Recently, the HRFPN has achieved promising results for region-level ship detection in both inshore and offshore areas of SAR images [10]. The HRFPN invariably maintains HR feature maps by connecting parallel high-to-low resolution convolutions, and repeatedly exchange the information between multi-resolution representations.…”

“…As in [10], the framework of the HRFPN consists of four stages of parallel convolution streams and an HRFPN block. A detailed description of the four-phase parallel convolutional flow can be found in the literature [10,40,41].…”

“…Thus, there are a total of 15 anchors on the pyramid. For other descriptions of RPN, please refer to [10,34].…”

“…In RPN, anchors are often involved. Following the literature [10,[33][34][35], the areas of the anchors are set to 32 2 , 64 2 , 128 2 , 256 2 , 512 2 pixels on five stages {P 2 , P 3 , P 4 , P 5 , P 6 } respectively, where P 6 is obtained via a 3 × 3 convolutional layer with a stride of 2 on P 5 . The anchors of multiple aspect ratios are used {1 : 2, 1 : 1, 2 : 1} at each stage.…”

“…Liu et al [9] proposed a multi-layer abstraction saliency model for airport detection in synthetic aperture radar (SAR) images. Wei et al [10] came up with a HR ship detection network (HR-SDNet) to perform precise and robust ship detection in SAR images. Deng et al [11] devised a method to detect multiscale artificial targets in remote sensing images.…”