With the continuous development of space and sensor technologies during the recent 40 years, ocean remote sensing has entered into the Big Data era with typical Five-V (volume, variety, value, velocity, and veracity) characteristics. Ocean remote sensing data archives reach several tens of petabytes, and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mining the useful information submerged in such ocean remote sensing data sets is a big challenge. Deep learning, a powerful technology recently emerging in the machine-learning field, has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image information extraction in many industrial-field applications and starts to draw interest in ocean remote sensing applications. In this review paper, we first systematically reviewed two deep learning frameworks that carry out ocean remote sensing image classifications and then presented eight typical applications in ocean internal wave/eddy/oil spill/coastal inundation/sea-ice/green algae/ship/coral reef mapping from different types of ocean remote sensing imagery to show how effective of these deep learning frameworks. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote sensing imagery.

No abstract

The great development of high-resolution SAR system gives more opportunities to observe building structures in detail, especially the advanced interferometric SAR (InSAR), which techniques attract more attention on exploiting useful information on urban infrastructures. Considering that the high-rise buildings in urban areas are quite common in big cities, it is of great importance to retrieve the three-dimension (3D) information of the urban high-rise buildings in urban remote sensing applications. In this paper, the 3D reconstruction of high-rise buildings using the wrapped InSAR phase image was studied, referring to the geometric modulation in very high resolution (VHR) SAR images, such as serious layover cause by high-rise buildings. Under the assumption of a rectangular shape, the high-rise buildings were detected and building façades were extracted based on the local frequency analysis of the layover fringe patterns. Then 3D information of buildings were finally extracted according to the detected façade geometry. Except for testing on a small urban area from the TanDEM-X data, the experiment carried on the single-pass InSAR wrapped phase in the wide urban scene, which was collected by the Chinese airborne N-SAR system, also demonstrated the possibility and applicability of the approach.

Near-inertial oscillation is an important physical process transferring surface wind energy into deep ocean. We investigated the near-inertial kinetic energy (NIKE) variability using acoustic Doppler current profi ler measurements from a mooring array deployed in the tropical western Pacifi c Ocean along 130°E at 8.5°N, 11°N, 12.6°N, 15°N, and 17.5°N from September 2015 to January 2018. Spatial features, decay timescales, and signifi cant seasonal variability of the observed NIKE were described. At the mooring sites of 17.5°N, 15°N, and 12.6°N, the NIKE peaks occurred in boreal autumn and the NIKE troughs were observed in boreal spring. By contrast, the NIKE at 11°N and 8.5°N showed peaks in winter and troughs in summer. Tropical cyclones and strong wind events played an important role in the emergence of high-NIKE events and explained the seasonality and latitudinal characteristics of the observed NIKE.