Development of convolutional neural network (CNN) optimized for object detection, led to significant developments in ship detection. Although training data critically affect the performance of the CNN-based training model, previous studies focused mostly on enhancing the architecture of the training model. This study developed a sophisticated and automatic methodology to generate verified and robust training data by employing synthetic aperture radar (SAR) images and automatic identification system (AIS) data. The extraction of training data initiated from interpolating the discretely received AIS positions to the exact position of the ship at the time of image acquisition. The interpolation was conducted by applying a Kalman filter, followed by compensating the Doppler frequency shift. The bounding box for the ship was constructed tightly considering the installation of the AIS equipment and the exact size of the ship. From 18 Sentinel-1 SAR images using a completely automated procedure, 7489 training data were obtained, compared with a different set of training data from visual interpretation. The ship detection model trained using the automatic training data obtained 0.7713 of overall detection performance from 3 Sentinel-1 SAR images, which exceeded that of manual training data, evading the artificial structures of harbors and azimuth ambiguity ghost signals from detection.

Oceanic internal waves are known to be important to the understanding of underwater acoustics, marine biogeochemistry, submarine navigation and engineering, and the Earth’s climate. In spite of the importance and increased knowledge of their ubiquity, the wave generation is still poorly understood in most parts of the world’s oceans. Here, we use satellite synthetic aperture radar images, in-situ observations, and numerical models to (1) show that wave energy (having relatively high amplitude) radiates from a shallow sill in the East China Sea in all directions, but with a significant time lag dependent on background conditions, (2) reveal that wave fronts are locally formed with often favorable conditions for re-initiation, and (3) demonstrate the resulting variety of wave patterns. These findings would be the case for any broad shelf having shallow sills with time-varying conditions, and therefore have significant implications on the redistribution of energy and materials in the global as well as regional ocean.

Coastal lands and nearshore marine areas are productive and rapidly changing places. However, these areas face many environmental challenges related to climate change and human-induced impacts. Space-borne remote sensing systems may be restricted in monitoring these areas because of their spatial and temporal resolutions. In situ measurements are also constrained from accessing the area and obtaining wide-coverage data. In these respects, airborne remote sensing sensors could be the most appropriate tools for monitoring these coastal areas. In this study, a cost-effective airborne remote sensing system with synthetic aperture radar and thermal infrared sensors was implemented to survey coastal areas. Calibration techniques and geophysical model algorithms were developed for the airborne system to observe the topography of intertidal flats, coastal sea surface current, sea surface temperature, and submarine groundwater discharge.

Eui-Ho Hwang, Jin-Gyeom Kim, Jang-Yong Sung, Ki-Mook Kang. Korean J. Remote Sens. -0001;0:. https://doi.org/10.7780/kjrs.2024.40.5.2.10