Detection of Ships Cruising in the Azimuth Direction Using Spotlight SAR Images with a Deep Learning Method

Spotlight synthetic aperture radar (SAR) achieves a high azimuth resolution with long integration times. Meanwhile, the long integration times also cause defocused and smeared images of moving objects such as cruising ships This is a typical imaging mechanism for moving objects in Spotlight SAR images. Conversely, ships can be classified as stationary or moving from the amount of smearing, and this classification method is, in general, based on manual observation. This paper proposes an automatic method for detecting cruising ships using deep learning known as the “You Only Look Once (YOLO) v5 model”, which is one of the frameworks of the YOLO family. In this study, ALOS-2/PALSAR-2 L-band Spotlight SAR images over the waters around the Miura Peninsula, Japan, were analyzed using the YOLO v5 model with a total of 53 ships’ images and compared with Automatic Identification System (AIS) data. The results showed a precision of approximately 0.85 and a recall rate of approximately 0.89 with an F-measure of 0.87. Thus, sufficiently high values were achieved in the automatic detection of moving ships using the deep learning method with the YOLO v5 model. As for false detections, images of breakwaters were classified as ships cruising in the azimuth direction. Further, range moving ships were found to be difficult to detect. From the present preliminary study, it was found that the YOLO v5 model is limited to ships cruising predominantly in the azimuth direction

Improved Accuracy of Velocity Estimation for Cruising Ships by Temporal Differences Between Two Extreme Sublook Images of ALOS-2 Spotlight SAR Images With Long Integration Times

A method for improving the estimation accuracy of the velocity of cruising ships is proposed using synthetic aperture radar (SAR) sublook images in the spotlight mode. The main purpose of spotlight SAR is to obtain high resolution utilizing longer integration times than those of other imaging modes, and the proposed method is based on these long integration times. The principal methodology is to produce successive N sublook images of a cruising ship, where N is more than approximately 10. The positions of the look-1 and look- N subimages differ by a substantial distance proportional to the cruising speed and the long interlook time difference. The distance, and hence the velocity of the cruising ship, can be computed from the cross-correlation function of these two sublook images with improved accuracy compared with other modes. We tested using PALSAR-2 spotlight subimages with N = 2, 10, and 20, and the results are compared with the automatic identification system data. Five images of ships cruising close to the azimuth direction were tested; the best result was obtained for the 10-look images with an average error of 13.8%, followed by 17.9% and 40.5% errors for the 20- and 2-look images, respectively. The reason is also given for the best result of the 10-look case over the 20-look case

Detection of Ships Cruising in the Azimuth Direction Using Spotlight SAR Images with a Deep Learning Method

Spotlight synthetic aperture radar (SAR) achieves a high azimuth resolution with long integration times. Meanwhile, the long integration times also cause defocused and smeared images of moving objects such as cruising ships This is a typical imaging mechanism for moving objects in Spotlight SAR images. Conversely, ships can be classified as stationary or moving from the amount of smearing, and this classification method is, in general, based on manual observation. This paper proposes an automatic method for detecting cruising ships using deep learning known as the “You Only Look Once (YOLO) v5 model”, which is one of the frameworks of the YOLO family. In this study, ALOS-2/PALSAR-2 L-band Spotlight SAR images over the waters around the Miura Peninsula, Japan, were analyzed using the YOLO v5 model with a total of 53 ships’ images and compared with Automatic Identification System (AIS) data. The results showed a precision of approximately 0.85 and a recall rate of approximately 0.89 with an F-measure of 0.87. Thus, sufficiently high values were achieved in the automatic detection of moving ships using the deep learning method with the YOLO v5 model. As for false detections, images of breakwaters were classified as ships cruising in the azimuth direction. Further, range moving ships were found to be difficult to detect. From the present preliminary study, it was found that the YOLO v5 model is limited to ships cruising predominantly in the azimuth direction

Time-Domain Simulation of Along-Track Interferometric SAR for Moving Ocean Surfaces

A time-domain simulation of along-track interferometric synthetic aperture radar (AT-InSAR) has been developed to support ocean observations. The simulation is in the time domain and based on Bragg scattering to be applicable for moving ocean surfaces. The time-domain simulation is suitable for examining velocities of moving objects. The simulation obtains the time series of microwave backscattering as raw signals for movements of ocean surfaces. In terms of realizing Bragg scattering, the computational grid elements for generating the numerical ocean surface are set to be smaller than the wavelength of the Bragg resonant wave. In this paper, the simulation was conducted for a Bragg resonant wave and irregular waves with currents. As a result, the phases of the received signals from two antennas differ due to the movement of the numerical ocean surfaces. The phase differences shifted by currents were in good agreement with the theoretical values. Therefore, the adaptability of the simulation to observe velocities of ocean surfaces with AT-InSAR was confirmed

SAR Image Simulation in the Time Domain for Moving Ocean Surfaces

This paper presents a fundamental simulation method to generate synthetic aperture radar (SAR) images for moving ocean surfaces. We have designed the simulation based on motion induced modulations and Bragg scattering, which are important features of ocean SAR images. The time domain simulation is able to obtain time series of microwave backscattering modulated by the orbital motions of ocean waves. Physical optics approximation is applied to calculate microwave backscattering. The computational grids are smaller than transmit microwave to demonstrate accurate interaction between electromagnetic waves and ocean surface waves. In this paper, as foundations for SAR image simulation of moving ocean surfaces, the simulation is carried out for some targets and ocean waves. The SAR images of stationary and moving targets are simulated to confirm SAR signal processing and motion induced modulation. Furthermore, the azimuth signals from the regular wave traveling to the azimuth direction also show the azimuthal shifts due to the orbital motions. In addition, incident angle dependence is simulated for irregular wind waves to compare with Bragg scattering theory. The simulation results are in good agreement with the theory. These results show that the simulation is applicable for generating numerical SAR images of moving ocean surfaces

SAR Image Simulation in the Time Domain for Moving Ocean Surfaces

This paper presents a fundamental simulation method to generate synthetic aperture radar (SAR) images for moving ocean surfaces. We have designed the simulation based on motion induced modulations and Bragg scattering, which are important features of ocean SAR images. The time domain simulation is able to obtain time series of microwave backscattering modulated by the orbital motions of ocean waves. Physical optics approximation is applied to calculate microwave backscattering. The computational grids are smaller than transmit microwave to demonstrate accurate interaction between electromagnetic waves and ocean surface waves. In this paper, as foundations for SAR image simulation of moving ocean surfaces, the simulation is carried out for some targets and ocean waves. The SAR images of stationary and moving targets are simulated to confirm SAR signal processing and motion induced modulation. Furthermore, the azimuth signals from the regular wave traveling to the azimuth direction also show the azimuthal shifts due to the orbital motions. In addition, incident angle dependence is simulated for irregular wind waves to compare with Bragg scattering theory. The simulation results are in good agreement with the theory. These results show that the simulation is applicable for generating numerical SAR images of moving ocean surfaces

Monitoring of cage-cultured sea cucumbers using an underwater time-lapse camera and deep learning-based image analysis

Optical cameras are being used to identify marine species as an easy and cost-effective aquaculture method for fishermen. Deep learning-based image analysis is now widely used in many fields, including aquaculture.In this study, combining an underwater time-lapse camera and deep learning-based image analysis, we proposed a simple monitoring system for cage-cultivated small sea cucumbers on the seafloor. With the camera, we obtained numerous time-lapse images of sea cucumbers for approximately two months.For these numerous images, the application of deep learning methods is beneficial for efficient and rapid analysis. In the monitored images, however, the outline of the sea cucumbers is likely to be blurred for three reasons: their small size, the net in the background, and the relatively low resolution of the camera system.It appears challenging to automatically detect sea cucumbers with blurred outlines. First, 1,429 individual sea cucumber images were manually annotated for training and validation purposes, using the YOLOv5 model. The training model achieved a precision, recall rate, and F-measure of 0.72, 0.71, and 0.72, respectively.The validation model successfully counted sea cucumbers in a cage at an acceptable monitoring level, despite the low image resolution. Second, all collected camera images are analyzed for automated detection using the tailored model.As a result, sea cucumbers mostly gathered around the top corner of the cage, probably because of the low current. The finding is useful for integrated multi-trophic aquaculture systems, especially the design of sea cucumber cages placed on the seafloor