Ship detection from satellite imagery using Deep Learning (DL) is an indispensable solution for maritime surveillance. However, applying DL models trained on one dataset to others having differences in spatial resolution and radiometric features requires many adjustments. To overcome this issue, this paper focused on the DL models trained on datasets that consist of different optical images and a combination of radar and optical data. When dealing with a limited number of training images, the performance of DL models via this approach was satisfactory. They could improve 5-20% of average precision, depending on the optical images tested. Likewise, DL models trained on the combined optical and radar dataset could be applied to both optical and radar images. Our experiments showed that the models trained on an optical dataset could be used for radar images, while those trained on a radar dataset offered very poor scores when applied to optical images.
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery
Global Fishing Watch（全球渔业观察）；Cambrio；Defense Innovation Unit（国防创新单元）；UC Berkeley（加州大学伯克利分校）
全世界不可持续的捕捞做法对海洋资源和生态系统构成重大威胁。识别在传统监测系统中没有出现的船只-被称为"黑暗船只"-是管理和保障海洋环境健康的关键。随着基于卫星的合成孔径雷达（SAR）成像和现代机器学习（ML）的兴起，现在可以在全天候条件下白天或晚上自动检测黑暗船只。然而，SAR图像需要特定领域的处理，并且ML社区无法广泛访问。海洋物体（船舶和海上基础设施）相对较小且稀疏，这对传统的计算机视觉方法构成了挑战。我们提出了用于训练ML模型的最大标记数据集，以检测和表征SAR图像中的船只和海洋结构。xView3-SAR由来自Sentinel-1任务的近1，000张可供分析的SAR图像组成，平均每张图像为29，400 x 24，400像素。使用自动和手动分析的组合对图像进行注释。每一幅SAR图像都附有同地测深和风态栅格。我们还提供了一个概述的xView3计算机视觉挑战赛，一个国际竞争使用xView3-SAR船舶检测和表征在大规模。我们发布数据（\href{https://iuu.xview.us/}{https://iuu.xview.us/}）和代码（\href{https://github.com/DIUx-xView}{https://github.com/DIUx-xView}），以支持这一重要应用的ML方法的持续开发和评估。
RSTNet: Enhancing Small-Target Recognition in Noisy SAR Imagery via Robust Feature Learning and Distribution-Aware Regression
College of Mathematics and Information Science, Anyang Institute of Technology（数学与信息科学学院，安阳职业技术学院）；School of Computer Science and Information Engineering, Anyang Institute of Technology（计算机科学与信息工程学院，安阳职业技术学院）；College of Electronic Information and Optical Engineering, Nankai University（电子信息与光学工程学院，南开大学）；Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology（天津光电传感器与传感网络技术重点实验室）；Beijing Xiao Mi Mobile Software Co., Ltd.（北京小米移动软件有限公司）；Anyang Embedded Intelligent Robot Co., Ltd.（安阳嵌入式智能机器人有限公司）
Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images
Max-Delbrueck-Center for Molecular Medicine in the Helmholtz Association（马克斯·德尔布鲁克医学研究中心（马克斯·德尔布鲁克医学研究中心））；Humboldt University of Berlin（柏林洪堡大学）；University of Potsdam（波茨坦大学）；Helmholtz Imaging（海德堡成像）；German Centre for Cardiovascular Research (DZHK)（德国心脏病研究中心（DZHK））；Experimental and Clinical Research Center (ECRC)（实验与临床研究中心（ECRC））
