Ship detection in optical remote sensing images 1 has potential applications in national maritime security, fishing, 2 and defense. Many detectors, including computer vision and 3 geoscience-based methods, have been proposed in the past decade. 4 Recently, deep learning-based algorithms have also achieved 5 great success in the field of ship detection. However, most of 6 the existing detectors face difficulties in complex environments, 7 small ship detection, and fine-grained ship classification. One 8 reason is that existing datasets have shortcomings in terms of 9 the inadequate number of images, few ship categories, image diversity, and insufficient variations. This paper publishes a 11 public ship detection dataset, namely ShipRSImageNet, which 12 contributes an accurately labeled dataset in different scenes with 13 variant categories and image sources. The proposed ShipRSIm-ageNet contains over 3,435 images with 17,573 ship instances in 15 50 categories, elaborately annotated with both horizontal and 16 orientated bounding boxes by experts. From our knowledge, up 17 to now, the proposed ShipRSImageNet is the largest remote 18 sensing dataset for ship detection. Moreover, several state-of-19 the-art detection algorithms are evaluated on our proposed 20 ShipRSImageNet dataset to give a benchmark for deep learning-21 based ship detection methods, which is valuable for assessing 22 algorithm improvement. The dataset has been released at https:

No abstract

Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.

Abstract:In a traditional registration of a single aerial image with airborne light detection and ranging (LiDAR) data using linear features that regard line direction as a control or linear features as constraints in the solution, lacking the constraint of linear position leads to the error propagation of the adjustment model. To solve this problem, this paper presents a line vector-based registration mode (LVR) in which image rays and LiDAR lines are expressed by a line vector that integrates the line direction and the line position. A registration equation of line vector is set up by coplanar imaging rays and corresponding control lines. Three types of datasets consisting of synthetic, theInternational Society for Photogrammetry and Remote Sensing (ISPRS) test project, and real aerial data are used. A group of progressive experiments is undertaken to evaluate the robustness of the LVR. Experimental results demonstrate that the integrated line direction and the line position contributes a great deal to the theoretical and real accuracies of the unknowns, as well as the stability of the adjustment model. This paper provides a new suggestion that, for a single image and LiDAR data, registration in urban areas can be accomplished by accommodating rich line features.

No abstract