• Journal of the Korea Computer Graphics Society
• /
• v.25 no.4
• /
• pp.9-16
• /
• 2019

In this paper, we propose a hydrodynamic scene separation method for wave propagation from video imagery using autoencoder. In the coastal area, image analysis methods such as particle tracking and optical flow with video imagery are usually applied to measure ocean waves owing to some difficulties of direct wave observation using sensors. However, external factors such as ambient light and weather conditions considerably hamper accurate wave analysis in coastal video imagery. The proposed method extracts hydrodynamic scenes by separating only the wave motions through minimizing the effect of ambient light during wave propagation. We have visually confirmed that the separation of hydrodynamic scenes is reasonably well extracted from the ambient light and backgrounds in the two videos datasets acquired from real beach and wave flume experiments. In addition, the latent representation of the original video imagery obtained through the latent representation learning by the variational autoencoder was dominantly determined by ambient light and backgrounds, while the hydrodynamic scenes of wave propagation independently expressed well regardless of the external factors.

• Journal of the Korea Computer Graphics Society
• /
• v.25 no.5
• /
• pp.1-9
• /
• 2019

In this paper, we propose a video enhancement method using generative adversarial networks to remove raindrops and restore the background information on the removed region in the coastal wave video imagery distorted by raindrops during rainfall. Two experimental models are implemented: Pix2Pix network widely used for image-to-image translation and Attentive GAN, which is currently performing well for raindrop removal on a single images. The models are trained with a public dataset of paired natural images with and without raindrops and the trained models are evaluated their performance of raindrop removal and background information recovery of rainwater distortion of coastal wave video imagery. In order to improve the performance, we have acquired paired video dataset with and without raindrops at the real coast and conducted transfer learning to the pre-trained models with those new dataset. The performance of fine-tuned models is improved by comparing the results from pre-trained models. The performance is evaluated using the peak signal-to-noise ratio and structural similarity index and the fine-tuned Pix2Pix network by transfer learning shows the best performance to reconstruct distorted coastal wave video imagery by raindrops.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.21 no.2
• /
• pp.154-163
• /
• 2009

Incident wave angles are conventionally estimated by the directional spectrum analysis of wave data collected from in-situ sensors. The in-situ measurements are limited in monitoring incident wave angles in the wide surf zone, since the techniques are typically expensive, labor-intensive, and point-measuring. In this study, estimation of incident wave angles using wave crest features captured in digital video imagery is proposed to observe incident wave directions over the surf zone. Line signatures of wave crests having high image pixel intensities are extracted by moving an interrogation window to identify high intensity pixels in sequential video images. Wave angles are computed by taking the first derivative of the extracted crest signatures, i.e. local slope of the crest signatures in the two-dimensional physical plane. Compared to the wave angle estimates obtained by the directional spectrum analysis, video-based wave angle estimates show good agreements in general.

• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.20 no.4
• /
• pp.333-341
• /
• 2008

A remote sensing technique to identify trajectories of breaking waves in the surf zone using oblique digital nearshore videos is proposed. The noise arising from white foam induced by wave breaking has hindered accurate remote sensing of wave properties in the surf zone. For this reason, this paper focuses on image processing to remove the noise and wave trajectory identification essential for wave property estimation. The nearshore video imagery sampled at 3 Hz are used, covering length scale(100 m). Original image sequences are processed through image frame differencing and directional low-pass image filtering to remove the noise characterized by high frequencies in the video imagery. The extraction of individual wave crest features is conducted using a Radon transform-based line detection algorithm in the processed cross-shore image timestacks having a two-dimensional space-time domain. The number of valid wave crest trajectories identified corresponds to about 2/3 of waves recorded by the in-situ sensors.

• Journal of Korea Water Resources Association
• /
• v.56 no.spc1
• /
• pp.1027-1036
• /
• 2023

Coastal topographic information is crucial in coastal management, but point measurment based approeaches, which are labor intensive, are generally applied to land and underwater, separately. This study introduces an efficient method enabling land and undetwater surveys using an unmanned aerial vehicle (UAV). This method involves applying two different algorithms to measure the topography on land and water depth, respectively, using UAV imagery and merge them to reconstruct whole coastal digital elevation model. Acquisition of the landside terrain is achieved using the Structure-from-Motion Multi-View Stereo technique with spatial scan imagery. Independently, underwater bathymetry is retrieved by employing a depth inversion technique with a drone-acquired wave field video. After merging the two digital elevation models into a local coordinate, interpolation is performed for areas where terrain measurement is not feasible, ultimately obtaining a continuous nearshore terrain. We applied the proposed survey technique to Jangsa Beach, South Korea, and verified that detailed terrain characteristics, such as berm, can be measured. The proposed UAV-based survey method has significant efficiency in terms of time, cost, and safety compared to existing methods.

• Ocean and Polar Research
• /
• v.31 no.1
• /
• pp.111-119
• /
• 2009

Biogenic surface films, which are often present in coastal areas, may enhance the signatures of hydrodynamic processes in microwave, optical, and infrared imagery. We analyzed ERS-1/2 Synthetic Aperture Radar (SAR) and Envisat Advanced Synthetic Aperture Radar (ASAR) images taken over the Japan/East Sea (JES). We focused on the appearance of the contrast SAR signatures, particularly the dark features of different scales caused by various oceanic and atmospheric phenomena. Spiral eddies of different scales were detected through surface film patterns both near the coast and in the open regions of the JES in warm and cold seasons. During field experiments carried out at the Pacific Oceanological Institute (POI) Marine Station 'Cape Shults' in Peter the Great Bay, the sea surface roughness characteristics were measured during the day and night using a developed polarization spectrophotometer and various digital cameras and systems of floats. The velocity of natural and artificial slicks was estimated using video and ADCP time series of tracers deployed on the sea surface. The slopes of gravity-capillary wave power spectra varied between .4 and .5. Surface currents in the natural and artificial slicks increased with the distance from the coast, varying between 4 and 40 cm/s. The contrast of biogenic and anthropogenic slicks detected on vertical and horizontal polarization images against the background varied over a wide range. SAR images and ancillary satellite and field data were processed and analyzed using specialized GIS for marine coastal areas.