A Coarse-to-Fine Network for Ship Detection in Optical Remote Sensing Images

Abstract

With the increasing resolution of optical remote sensing images, ship detection in optical remote sensing images has attracted a lot of research interests. The current ship detection methods usually adopt the coarse-to-fine detection strategy, which firstly extracts low-level and manual features, and then performs multi-step training. Inadequacies of this strategy are that it would produce complex calculation, false detection on land and difficulty in detecting the small size ship. Aiming at these problems, a sea-land separation algorithm that combines gradient information and gray information is applied to avoid false alarms on land, the feature pyramid network (FPN) is used to achieve small ship detection, and a multi-scale detection strategy is proposed to achieve ship detection with different degrees of refinement. Then the feature extraction structure is adopted to fuse different hierarchical features to improve the representation ability of features. Finally, we propose a new coarse-to-fine ship detection network (CF-SDN) that directly achieves an end-to-end mapping from image pixels to bounding boxes with confidences. A coarse-to-fine detection strategy is applied to improve the classification ability of the network. Experimental results on optical remote sensing image set indicate that the proposed method outperforms the other excellent detection algorithms and achieves good detection performance on images including some small-sized ships and dense ships near the port.

YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images

“…[19][20][21]; detecting and tracking moving objects (such as vehicles, ships, etc.) [22][23][24][25]; and, detecting endangered species (e.g., wildlife animals, sea mammals, etc.) [26,27].…”

A Novel Coarse-to-Fine Method of Ship Detection in Optical Remote Sensing Images Based on a Deep Residual Dense Network

“…Nevertheless, ship detection performance in optical remote sensing images usually suffers from three main problems: (1) a large data volume of optical remote sensing imagery [15,18,19], (2) the interference of complex factors such as clouds and strong waves [15,16,19,20], and (3) the large-scale variation of ships (from dozens of pixels to thousands) [16,20]. In the past few years, many ship detection algorithms [21][22][23][24][25] have been proposed.…”

Ship Detection in Multispectral Satellite Images Under Complex Environment

“…Ship detection in remote-sensing imagery is of significant importance in maritime security and transportation surveillance applications, such as vessel salvage and fisheries management [1][2][3]. As revisit periods have decreased, along with the improvements in image resolution for optical satellites, continuous monitoring over a vast area via images has become a reality through sensors embedded within these satellites.…”

“…Both methods were applied in RGB color space images. The methods in References [3,[19][20][21] combined the CNN technique with the coarse-to-fine strategies and believed that this would perform better in remote-sensing image-detection tasks. They all took full advantage of the spatial feature representation of remote-sensing images and benefitted from a large data set or the large size (increased depth) of the network to improve their performance under different situations (e.g., References [11][12][13]) but ignored the essential difference between remote-sensing images and natural scene images.…”