SAR sensors measurements for environmental classification: Machine learning-based performances

Lay-Ekuakille, A.; Ugwiri, Moise Avoci; Okitadiowo, John Peter Djungha; Telesca, Vito; Picuno, Pietro; Liguori, Consolatina; Singh, Satya P.

doi:10.1109/mim.2020.9200877

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…A hand position identification in images using the support of a faster R-CNN object detector is implemented in [ 30 ]. The importance of deep learning algorithms in the sensor-based environmental monitoring field is proposed in [ 31 ]. The effectiveness of deep learning methods is evaluated over fluid images and an extraction technique for velocity fields from images is discussed in [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

An Efficient Mobile Application for Identification of Immunity Boosting Medicinal Plants using Shape Descriptor Algorithm

Thanikkal

Dubey

Thomas

2023

Wireless Pers Commun

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Section: Methodsmentioning

confidence: 99%

An Efficient Mobile Application for Identification of Immunity Boosting Medicinal Plants using Shape Descriptor Algorithm

Thanikkal

Dubey

Thomas

2023

Wireless Pers Commun

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“…By comparing Figure 2 a,b, it can be found that the use of subaperture segmentation technology to improve the refocusing effect of dominant waves will lead to a decrease in the signal-to-noise ratio of the SAR image. Therefore, to obtain the optimum subaperture, this paper proposed a new evaluation, namely,

, referring to the calculation method of the F -measure [ 36 , 37 ] in machine learning, which considers the peak-to-background ratio and the equivalent number of looks. The

can be calculated by:

among them,

…”

Section: Sar Imaging Algorithm Of Ocean Waves Based On Optimum Subape...mentioning

confidence: 99%

SAR Imaging Algorithm of Ocean Waves Based on Optimum Subaperture

Zhao

Wei

Chong

et al. 2022

Sensors

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Synthetic Aperture Radar (SAR) is widely applied to the field of ocean remote sensing. Clear SAR images are the basis for ocean information acquisitions, such as parameter retrieval of ocean waves and wind field inversion of the ocean surface. However, the SAR ocean images are usually blurred, which seriously affects the acquisition of ocean information. The reasons for the wave blurring in SAR images mainly include the following two aspects. One is that when SAR observes the ocean, the motion of ocean waves will have a greater impact on imaging quality. The other is that the ocean’s surface is seriously decorrelated within the integration time. In order to obtain clear SAR images of ocean waves, a SAR imaging algorithm of ocean waves based on the optimum subaperture is proposed, aiming at the above two aspects. The optimum focus setting of the ocean waves is calculated, drawing support from the azimuth phase velocity of the dominant wave. The optimum subaperture is further calculated according to the proposed new evaluation, namely, F. Finally, according to the optimum focus setting and the optimum subaperture, the dominant wave is refocused, and a clear SAR image of the dominant wave can be obtained. The proposed algorithm was applied to airborne L-band and P-band SAR data. Furthermore, the proposed algorithm was compared with present methods, and the results sufficiently demonstrated the effectiveness and superiority of the proposed algorithm.

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“…Remote sensing is generally the common technique to measure sea parameters, especially those related to waves. Radar is the most important instrument for detecting surface wave properties, for instance in the X-band, hence radar reflectivity can display these properties [ 3 ]. However, airborne or satellite-based platforms display some limitations such as height and length sensitivity, and information acquisition by points instead of surface; this implies the need to extend the local punctual information to the whole adjacent area.…”

Section: Introductionmentioning

confidence: 99%

Image Sensors for Wave Monitoring in Shore Protection: Characterization through a Machine Learning Algorithm

Lay-Ekuakille

Okitadiowo

Luccio

et al. 2021

Sensors

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Waves propagating on the water surface can be considered as propagating in a dispersive medium, where gravity and surface tension at the air–water interface act as restoring forces. The velocity at which energy is transported in water waves is defined by the group velocity. The paper reports the use of video-camera observations to study the impact of water waves on an urban shore. The video-monitoring system consists of two separate cameras equipped with progressive RGB CMOS sensors that allow 1080p HDTV video recording. The sensing system delivers video signals that are processed by a machine learning technique. The scope of the research is to identify features of water waves that cannot be normally observed. First, a conventional modelling was performed using data delivered by image sensors together with additional data such as temperature, and wind speed, measured with dedicated sensors. Stealth waves are detected, as are the inverting phenomena encompassed in waves. This latter phenomenon can be detected only through machine learning. This double approach allows us to prevent extreme events that can take place in offshore and onshore areas.

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SAR sensors measurements for environmental classification: Machine learning-based performances

Cited by 5 publications

References 13 publications

An Efficient Mobile Application for Identification of Immunity Boosting Medicinal Plants using Shape Descriptor Algorithm

An Efficient Mobile Application for Identification of Immunity Boosting Medicinal Plants using Shape Descriptor Algorithm

SAR Imaging Algorithm of Ocean Waves Based on Optimum Subaperture

Image Sensors for Wave Monitoring in Shore Protection: Characterization through a Machine Learning Algorithm

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