In 1978, the SEASAT satellite was launched, carrying the first civilian synthetic aperture radar (SAR). The mission was the monitoring of ocean: application to land was also studied. Despite its short operational time of 105 days, SEASAT-SAR provided a wealth of information on land and sea, and initiated many spaceborne SAR programs using not only the image intensity data, but also new technologies of interferometric SAR (InSAR) and polarimetric SAR (PolSAR). In recent years, artificial intelligence (AI), such as deep learning, has also attracted much attention. In the present article, a review is given on the imaging processes and analyses of oceanic data using SAR, InSAR, PolSAR data and AI. The selected oceanic phenomena described here include ocean waves, internal waves, oil slicks, currents, bathymetry, ship detection and classification, wind, aquaculture, and sea ice.

A theory of multiaperture along-track interferometric (MA-ATI) synthetic aperture radar (SAR) was previously proposed to estimate the velocity vector of moving targets by utilizing conventional ATI SAR data. In the MA-ATI SAR method, multi/sublook processing is applied to both the raw data acquired by the fore-and-aft antennas, yielding two sets of forward-looking images and two sets of backward-looking images. From the interferograms of corresponding directions, two velocity components can be estimated, and hence the moving directions of a target are also obtained. The velocity vector is calculated by combining the estimated moving direction with the velocity in the range direction measured by the conventional ATI method. In order to show initial validation of this theory, a numerical SAR simulator, which has a function to produce a time series SAR raw data, was further developed to simulate MA-ATI SAR by taking into account the multilook processing. In the present validation study, this simulator (in time-domain processing) was applied to estimate the moving directions of a moving point target and ocean currents in the case of an airborne radar system. In particular, the effect on the subaperture azimuth beam angle was discussed since it is a key factor for the estimation. The preliminary results demonstrated that the estimation accuracy exhibited a direction error of 5°-10° for both the moving target and ocean currents, respectively, when half of the subaperture azimuth beam angle was larger than 1.25°.

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.