Performance Analysis of Internal Solitary Wave Detection and Identification Based on Compact Polarimetric SAR

Zhang, Hao; Meng, Junmin; Sun, Lina; Zhang, Xi; Shu, Sijing

doi:10.1109/access.2020.3025946

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…The ISWs-sea surface variation for the various polarization features has also been investigated in this study. The results showed the improvement of the k-means clustering algorithm based on CP features to detect ISWs [216]. Moreover, Ning et al [217] evaluated the spatial scattering properties of internal solitary waves using Sentinel-1 and GF-3 SAR images.…”

Section: ) Ocean Surface Wind (Osw)mentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

Amani¹,

Mohseni

Layegh

et al. 2022

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

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As discussed in the previous part of this review paper, Remote Sensing (RS) creates unprecedented opportunities by providing a variety of systems with different characteristics to study and monitor oceans. Part 1 of this review paper was dedicated to reviewing passive RS systems and their main applications in the ocean. Here, in part 2, seven active RS systems, including scatterometers, altimeters, gravimeters, Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR), Sound Navigation and Ranging (SONAR), High-Frequency (HF) radars are comprehensively reviewed. For consistency, this part is structured similarly to part 1. The aforementioned systems, along with their characteristics and primary applications, are introduced in separate sections. This review paper provides useful information to all students and researchers who are interested in the oceanographic applications of active RS systems.

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Section: ) Ocean Surface Wind (Osw)mentioning

confidence: 99%

“…Therefore, in [216], the application of fullpolarimetric ALOS PALSAR images was investigated to identify ocean Internal Solitary Waves (ISWs) [216]. This study was developed to construct compact polarimetry SAR images and to derive different CP features.…”

Section: ) Ocean Surface Wind (Osw)mentioning

confidence: 99%

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

Amani¹,

Mohseni

Layegh

et al. 2022

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

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show abstract

“…The framework has been demonstrated and tested using six satellite images of the Eastern Atlantic, affirming its capability to determine the signature type and wavelength of the internal wave. The study conducted by Zhang et al [16] investigates the utilization of compact polarimetric (CP) SAR in detecting and identifying oceanic internal solitary waves (ISWs). CP SAR images are generated and 26 CP features are extracted from fullpolarimetric Advanced Land Observing Satellite (ALOS) Phase Array type L-band SAR (PALSAR) images.…”

Section: Introductionmentioning

confidence: 99%

Stripe Extraction of Oceanic Internal Waves Using PCGAN with Small-Data Training

Duan,

Barintag,

Meng

et al. 2024

Remote Sensing

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Playing a crucial role in ocean activities, internal solitary waves (ISWs) are of significant importance. Currently, the use of deep learning for detecting ISWs in synthetic aperture radar (SAR) imagery is gaining growing attention. However, these approaches often demand a considerable number of labeled images, which can be challenging to acquire in practice. In this study, we propose an innovative method employing a pyramidal conditional generative adversarial network (PCGAN). At each scale, it employs the framework of a conditional generative adversarial network (CGAN), comprising a generator and a discriminator. The generator works to produce internal wave patterns as authentically as possible, while the discriminator is designed to differentiate between images generated by the generator and reference images. The architecture based on pyramids adeptly captures the encompassing as well as localized characteristics of internal waves. The incorporation of upsampling further bolsters the model’s ability to recognize fine-scale internal wave stripes. These attributes endow the PCGAN with the capacity to learn from a limited amount of internal wave observation data. Experimental results affirm that the PCGAN, trained with just four internal wave images, can accurately detect internal wave stripes in the test set. Through comparative experiments with other segmentation models, we demonstrate the effectiveness and robustness of PCGAN.

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“…Therefore, in the SAR image, ISWs often appears as bright (convergence, rough) and dark (divergence, smooth) stripes [14]. Based on images captured by various SAR sensors, there are numerous reports on ISWs, such as generation sources, hydrodynamic parameter inversion, deep learning applications, and radar scattering characteristics [15], [16], [17], [18], [19], [20], [21], [22], [23], [24]. However, a full quantitative analysis of SAR backscattering properties of ISWs, particularly the scattering characteristics of different bands, varied polarization, and different observation conditions, is urgently required.…”

mentioning

confidence: 99%

Characterization of Polarized SAR Scattering of Breaking Waves Caused by Internal Solitary Waves

Zhang,

Meng,

Sun

et al. 2024

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

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Internal solitary waves (ISWs) resonate with meterscale surface waves, which causes crests to overturn and waves to break strongly, generating many whitecaps. We analyze the polarized SAR characteristics of ISWs based on nonpolarized scattering theory and polarization decomposition theory. The surface breaking waves induced by ISWs generate short-scale waves carrying Bragg waves on the one hand, which enhances the polarized Bragg scattering (PD) contribution at the decimeter or centimeter scale and generate additional nonpolarized scattering (NP) contribution through specular scattering on the other hand. The copolarized scattering intensity in ISW regions is always higher than that in unperturbed sea surface under both L and C bands, with higher PD and NP contributions. The average PD is 1.3 (1.7) times higher than that of the unperturbed sea surface, and the average NP is approximately 82% (165%) higher than that of the sea surface. The PD and NP values associated with the convergent zones are always larger than those associated with the divergent zones, indicating that breaking waves mainly occur in convergence. The results of HAα decomposition show that the scattering mechanism of ISWs is surface scattering with reduced scattering entropy and mean scattering angle. We construct compact polarization based on fully polarized SAR, and extract polarization parameters that indicate increased degree of polarization at ISWs, which supports our inference of enhanced specular scattering.

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Performance Analysis of Internal Solitary Wave Detection and Identification Based on Compact Polarimetric SAR

Cited by 10 publications

References 25 publications

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

Stripe Extraction of Oceanic Internal Waves Using PCGAN with Small-Data Training

Characterization of Polarized SAR Scattering of Breaking Waves Caused by Internal Solitary Waves

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