Ship detection plays a pivotal role in efficient marine monitoring, port management, and safe
navigation. However, the development of ship detection techniques is vastly behind other …

With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning
technologies, the application of UAV-based object detection has become increasingly …

To mine the spectral-spatial information of target pixel in hyperspectral image classification
(HSIC), convolutional neural network (CNN)-based models widely adopt patch-based input …

Certain fields present significant challenges when attempting to train complex Deep
Learning architectures, particularly when the available datasets are limited and imbalanced …

With the rapid development of low-end Internet of Things (IoT) devices and shipborne
sensors, efficient multi-source data fusion methods for autonomous surface vehicles (ASVs) …

Small-sized unmanned surface vehicles (USV) are coastal water devices with a broad range
of applications such as environmental control and surveillance. A crucial capability for …

The primary task of marine surveillance is to construct a perfect marine situational
awareness (MSA) system that serves to safeguard national maritime rights and interests and …

The progress of obstacle detection via semantic segmentation on unmanned surface
vehicles (USVs) has been significantly lagging behind the developments in the related field …

Existing instance segmentation models mostly pay less attention to the targeted
characteristics of ships in synthetic aperture radar (SAR) images, which hinders further …

Convolutional neural network (CNN) has demonstrated to be a powerful tool for
hyperspectral images (HSIs) classification. Previous CNN-based HSI classification methods …