A Novel Method of Ship Detection under Cloud Interference for Optical Remote Sensing Images

Abstract

In this paper, we propose a novel method developed for detecting incomplete ship targets under cloud interference and low-contrast ship targets in thin fog based on superpixel segmentation, and outline its application to optical remote sensing images. The detection of ship targets often requires the target to be complete, and the overall features of the ship are used for detection and recognition. When the ship target is obscured by clouds, or the contrast between the ship target and the sea-clutter background is low, there may be incomplete targets, which reduce the effectiveness of recognition. Here, we propose a new method combining constant false alarm rate (CFAR) and superpixel segmentation with feature points (SFCFAR) to solve the above problems. Our newly developed SFCFAR utilizes superpixel segmentation to divide large scenes into many small regions which include target regions and background regions. In remote sensing images, the target occupies a small proportion of pixels in the entire image. In our method, we use superpixel segmentation to divide remote sensing images into meaningful blocks. The target regions are identified using the characteristics of clusters of ship texture features and the texture differences between the target and background regions. This step not only detects the ship target quickly, but also detects ships with low contrast and under cloud cover. In optical remote sensing, ships at sea under thin clouds are not common in practice, and the sample size generated is relatively small, so this problem is not applicable to deep learning algorithms for training, while the SFCFAR algorithm does not require data training to complete the detection task. Experiments show that the proposed SFCFAR algorithm enhances the detection of obscured ship targets under clouds and low-contrast targets in thin fog, compared with traditional target detection methods and as deep learning algorithms, further complementing existing ship detection methods.

Knowledge-Transfer-Based Bidirectional Vessel Monitoring System for Remote and Nearshore Images

“…With increasing maritime activities and frequent vessel movements, the large-scale information on vessel activities poses an urgent challenge for the maritime surveillance system [4][5][6][7]. Current existing traditional vessel monitoring systems have security risks and cannot meet the demands of the maritime industry [8][9][10]. Traditional vessel monitoring systems can only identify vessels equipped with radio communication equipment such as AIS (automatic identification system), GPS (global positioning system), and VHF (very high frequency) that are considered intelligent, while small vessels or other non-intelligent vessels, such as fishing boats or sailing boats that lack such radio communication equipment, are imperceptible.…”