Internal Wave Detection and Characterization with SAR data

Surampudi, Samvedya; Sasanka, Sasamal

doi:10.1109/tengarss48957.2019.8976045

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…This is because they are sensitive to the surface roughness of the ocean. Tidal waves modulate the surface roughness of the ocean and can appear as vertical or horizontal stripes on SAR images [246], [247]. Additionally, the phase difference in SAR images due to the tidal wave propagation is useful for tidal monitoring [248].…”

Section: A Application-based Point Of Viewmentioning

confidence: 99%

Synthetic Aperture Radar (SAR) for Ocean: A Review

Asiyabi,

Ghorbanian,

Tameh

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Oceans cover approximately 71% of the Earth's surface and provide numerous services to the environment and humans. Precise, real-time, and large-scale monitoring of the oceanographic parameters is essential for ocean conservation and understanding the interactions between oceans and the atmosphere. In this regard, Synthetic Aperture Radar (SAR) systems, with unique capabilities (e.g., day-night and almost allweather data acquisition), provide valuable datasets for ocean studies. Many studies have exploited the applications of SAR imagery for oceans and have proposed numerous methods to study oceanographic parameters. In this study, a brief introduction to SAR and the interaction between microwave signals and the ocean surface are initially provided. Then, the important spaceborne and airborne SAR systems for oceanographic applications are summarized. Subsequently, 12 different applications of SAR systems in the ocean are comprehensively discussed, and the advantages and disadvantages of SAR systems for ocean studies are extensively explored. Finally, the research trend on SAR applications in the ocean is provided by analyzing all the relevant papers published between 1973 and the end of December 2022, and the existing challenges are discussed for future studies.

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Section: A Application-based Point Of Viewmentioning

confidence: 99%

Synthetic Aperture Radar (SAR) for Ocean: A Review

Asiyabi,

Ghorbanian,

Tameh

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The author represents that the observed waves are nonlinear waves with peak-topeak amplitude. The authors of [15] uses two methods for linear and nonlinear internal waves. Linear internal waves are detected using fast Fourier transform (FFT).…”

Section: Literature Surveymentioning

confidence: 99%

Ocean Internal Wave Detection from SAR Images using Particle Swarm Optimization

Divya

Vasavi

Sarma

2020

2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC)

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The internal waves are the waves that occur in the deep ocean. Many researchers found different methods for the detection of internal waves. As we all know that machine learning is the fastest growing technology to solve complicated problems. So, here in this paper we took this advantage for the detection of internal waves because this is also considered as a complicated problem. Based on this a novel method is proposed for internal wave detection using neural networks which is a method that acts as a human brain. A novel system using Particle swarm optimization is proposed in this paper to automatically detect internal waves. Such system can differentiate with surface wavelets and slicks. This paper describes extraction of internal wave parameters from SAR images. Initially ocean internal wave detection is modeled based on optimization algorithms. In the proposed method, two models such as feature representation model and prediction model will be build.

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“…Simonin et al (2009) put forward a framework based on wavelets, edge discrimination, edge linking and edge parallelism analysis to automatically identify possible IW packets in SAR images (Simonin et al, 2009). Surampudi and Sasanka (2019) used fast Fourier transform and continuous wavelet transform to detect and characterize IWs based on Sentinel-1 (S-1) SAR images (Surampudi & Sasanka, 2019). These image processing or signal processing techniques such as edge detector, wavelet transform, radon transform, etc.…”

mentioning

confidence: 99%

An Internal Waves Data Set From Sentinel‐1 Synthetic Aperture Radar Imagery and Preliminary Detection

Tao

Guo³

et al. 2022

Earth and Space Science

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Internal waves (IWs) are a widely occurring phenomenon in the global oceans. They have significant impacts on marine environments through their propagation mechanisms. In this study, we collect 390 satellite images with 775 labels from Sentinel‐1 Synthetic Aperture Radar (SAR) from 2014 to 2021 to construct a data set containing IW packets in Andaman Sea, South China Sea, Sulu Sea, and Celebes Sea, respectively. All the SAR images acquired are pre‐processed to provide clear IW packets for better visualization. To better detect IWs in global oceans automatically, a machine‐learning based model is proposed to focus on different channels and the spatial information. The precision, recall, and mean average precision of our improved model applied on the IW data set is up to 98.7%, 96.9%, and 98.9%, respectively. Various cases of IWs images are analyzed to illustrate detection quality in different stripes, scales, and propagation directions of IWs. The experimental results indicate that our data set is helpful to better detect IWs, and proposed network can accurately detect IWs in SAR images under various conditions.

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Internal Wave Detection and Characterization with SAR data

Cited by 7 publications

References 12 publications

Synthetic Aperture Radar (SAR) for Ocean: A Review

Synthetic Aperture Radar (SAR) for Ocean: A Review

Ocean Internal Wave Detection from SAR Images using Particle Swarm Optimization

An Internal Waves Data Set From Sentinel‐1 Synthetic Aperture Radar Imagery and Preliminary Detection

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