Rain Detection From X-Band Marine Radar Images: A Support Vector Machine-Based Approach

Chen, Xinwei; Huang, Weimin; Zhao, Chen; Tian, Yingwei

doi:10.1109/tgrs.2019.2953143

Cited by 77 publications

(28 citation statements)

References 38 publications

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“…The reflected signal produced by high waves is very intense and masks the possible presence of cetaceans [8]. Similarly, the presence of rain masks the radar signal reflected by the sea and, consequently, reduces the ability to detect targets [9].…”

Section: Methodsmentioning

confidence: 99%

X-Band Radar for Cetacean Detection (Focus on Tursiops truncatus) and Preliminary Analysis of Their Behavior

2020

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Cetaceans are protected species all over the world, most of them are vulnerable, endangered, or data deficient (according to International Union for Conservation of Nature -IUCN red list). X-band radars detect the echo of the electromagnetic signal reflected by an obstacle or a ship (target). The application of X-band radar to the detection of cetaceans is a new and innovative field of research that could improve the automation of marine mammal data collection, and this is the first time in the Mediterranean Sea. The aim of this work was to test the capability of X-band radar installed along the coast (ground-based) to detect and track cetaceans in a range of approximately 2.5 nautical miles from the radar antenna. Data collection included a part of field work, implemented through the acquisition of photographic images and target's radar detection (by the panoramic terrace Santa Maria in Corniglia), and a part, performed in the laboratory, of data analysis. The work was undertaken between May and November 2018. During this period, 30 days of monitoring were carried out (about 300 h) and about 10,000 radar images were recorded. The first results showed that we were able to recognize the target "cetacean" from the other common targets (boats, buoys, etc.) detected by the radar. In particular 70 dolphins were sighted by visual census; 12 of them were recognized on radar images. Radar images allowed extraction of dolphin dive time (between 2 and 15 s). The next step will be to allow the radar to identify the presence of marine mammals itself since it also works at night and with low visibility. This technique could complement the protection measures of cetaceans, highlighting their presence at sea even if it is impossible with waves higher than 0.8 m and over distances greater than 2.5 km.

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Section: Methodsmentioning

confidence: 99%

X-Band Radar for Cetacean Detection (Focus on Tursiops truncatus) and Preliminary Analysis of Their Behavior

2020

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“…There are other meteorological phenomena, like light rain, which may look similar to fog in some type of images. One of the valuable works in future could be detection of rain using radar data, using methods similar to what is described in [34], [35].…”

Section: Uncertaintiesmentioning

confidence: 99%

A Probability-Based Daytime Algorithm for Sea Fog Detection Using GOES-16 Imagery

Mahdavi¹,

Amani²,

Bullock³

et al. 2021

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

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“…It should be noted that surfaces under the same amount of radiation and with similar texture would have different values of σ and produce different gray levels. This noisy pattern of microwave imaging will be much more complicated when dealing with sea-ice terrains because of the complex electromagnetic interaction of different layers and the low backscattering profile of the sea-ice while being illuminated by active coherent radiation under different radiation scenarios [39]. Hence, an extended terrain backscattering modeling of the sea-ice on the basis of σ 0 (•) might be helpful for further understanding of the presence of N(t) within the backscattered signal as a major abnormal source of random pixels and the terrain electromagnetic interactions compared to previous works [26]- [41].…”

Section: A Kompsat-5 Sar Sea-ice Image Analysis and Resolutional Promentioning

confidence: 99%

Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method

Shahrezaei

Kim

2020

IEEE Access

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Texture in synthetic aperture radar (SAR) images is a combination of the intrinsic texture of scene backscattering and the texture due to noncoherent high-frequency multiplicative noise (HMN) interactions that reflect erroneous information and lead to observation misinterpretation. The focus of this paper is the fractal analysis of KOMPSAT-5 SAR images of noncoherent sea-ice textures while being decomposed by discrete wavelet transform (DWT) processing. As a novel approach, fractal analysis relies on SAR sea-ice spatial backscattering data generation and time-frequency domain (TFD) formulations from the perspective of uncorrelated HMN. To the best of our knowledge, this is the first time that the extraction of the resolution profile and raw data for the reference KOMPSAT-5 SAR sea-ice image have been derived, evaluated and compared both qualitatively and quantitatively at each scale of DWT decomposition on the basis of the presence of HMN. This paper also presents a novel detailed modeling of the multiresolution probability distribution function of the HMN and its power spectral density function modeling at each scale of the decomposition. Other quality assessment techniques, such as two K-means clustering algorithms and several visualized verification methods, such as gradient vector field, advection mapping and tensor field mapping, have been implemented in this regard to investigate embedded HMN suppression and its adverse effects on the presence of pixel anomalies. As a result, as the decomposition continues, the HMN at each scale of decomposition is constantly altering from high-frequency uncorrelated anomalies to low-frequency joint spatial information within the observed 2-D data. In other words, excessive multiscale HMN suppression will result in spatial information loss that makes the DWT scale selection quite important for texture classification. The results also show that the importance of HMN suppression in SAR sea-ice images in the form of pixel anomaly decomposition for the purpose of further texture investigation should be in accordance with the spectral behavior of the HMN. The results are helpful for SAR remote sensing image restoration and data preservation when dealing with high-resolution SAR images, such as in time series analysis, sea-ice texture change detection, and polar structural mapping. The proposed approach is implemented on real KOMPSAT-5 SAR satellite sea-ice images, while fractal spatial resolution profile simulations are carried out based on the inversed equalized hybrid domain image formation algorithm. INDEX TERMS Discrete wavelet transform, fractal analysis, high-frequency multiplicative noise, raw data generation, synthetic aperture radar.

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Rain Detection From X-Band Marine Radar Images: A Support Vector Machine-Based Approach

Cited by 77 publications

References 38 publications

X-Band Radar for Cetacean Detection (Focus on Tursiops truncatus) and Preliminary Analysis of Their Behavior

X-Band Radar for Cetacean Detection (Focus on Tursiops truncatus) and Preliminary Analysis of Their Behavior

A Probability-Based Daytime Algorithm for Sea Fog Detection Using GOES-16 Imagery

Fractal Analysis and Texture Classification of High-Frequency Multiplicative Noise in SAR Sea-Ice Images Based on a Transform- Domain Image Decomposition Method

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