A Deep-Learning Framework for the Detection of Oil Spills from SAR Data

Shaban, Mohamed; Salim, Reem; Khalifeh, Hadil Abu; Khelifi, Adel; Shalaby, Ahmed; El-Mashad, Shady Y.; Mahmoud, Ali; Ghazal, Mohammed; El-Baz, Ayman

doi:10.3390/s21072351

Cited by 63 publications

(25 citation statements)

References 31 publications

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“…Discriminating HC from PD OFF medication and PD ON medication. The deep learning approach adopted in this study uses an efficient CNN that was recently proposed by Shaban et al and validated on a computer vision application (i.e., detection of oil spill from satellite aperture radar images) [ 54 ]. The components of the CNN are listed in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

Resting-state electroencephalography based deep-learning for the detection of Parkinson’s disease

Shaban

Amara

2022

PLoS ONE

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Parkinson’s disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower efficacy of therapeutic treatments. In this study, we introduce a deep-learning approach based on a recently-proposed 20-Layer Convolutional Neural Network (CNN) applied on the visual realization of the Wavelet domain of a resting-state EEG. The proposed approach was able to efficiently and accurately detect PD as well as distinguish subjects with PD on medications from subjects who are off medication. The gradient-weighted class activation mapping (Grad-CAM) was used to visualize the features based on which the approach provided the predictions. A significantly high accuracy, sensitivity, specificity, AUC, and Weighted Kappa Score up to 99.9% were achieved and the visualization of the regions in the Wavelet images that contributed to the deep-learning approach decisions was provided. The proposed framework can then serve as an effective computer-aided diagnostic tool that will support physicians and scientists in further understanding the nature of PD and providing an objective and confident opinion regarding the clinical diagnosis of the disease.

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Section: Methodsmentioning

confidence: 99%

Resting-state electroencephalography based deep-learning for the detection of Parkinson’s disease

Shaban

Amara

2022

PLoS ONE

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“…The road data were the vector data format of ESRI (RL). NDVI data use MOD13 A2 16D 1 km spatial resolution (collection 6) data (https://earthdata.nasa.gov) [44]. Other types included the day of the year (DOY).…”

Section: Auxiliary Datamentioning

confidence: 99%

“…Indeed, authors in [39][40][41][42][43] proposed different neural network-based methods (like CNN and Deep NN) in order to improve oil spill detection and classification. Some other notable interesting CNN-based oil spill detection and classification frameworks include the works in [44,45].…”

Section: Related Research Workmentioning

confidence: 99%

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

2022

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“…An example of a single-stage method was proposed in [31], where a CNN (A-ConvNets) performs the classification using SAR images. In [32], a two-stage framework based on two CNNs was proposed to semantically segment SAR images. In [33], the authors presented a three-stage method based on a Mask R-CNN model that is able to identify different Region of Interests (ROIs) and segment them, obtaining state-of-the-art results in terms of classification ability.…”

Section: Related Workmentioning

confidence: 99%

Oil Spill Identification from SAR Images for Low Power Embedded Systems Using CNN

Diana

Fanucci

2021

Remote Sensing

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Oil spills represent one of the major threats to marine ecosystems. Satellite synthetic-aperture radar (SAR) sensors have been widely used to identify oil spills due to their ability to provide high resolution images during day and night under all weather conditions. In recent years, the use of artificial intelligence (AI) systems, especially convolutional neural networks (CNNs), have led to many important improvements in performing this task. However, most of the previous solutions to this problem have focused on obtaining the best performance under the assumption that there are no constraints on the amount of hardware resources being used. For this reason, the amounts of hardware resources such as memory and power consumption required by previous solutions make them unsuitable for remote embedded systems such as nano and micro-satellites, which usually have very limited hardware capability and very strict limits on power consumption. In this paper, we present a CNN architecture for semantically segmenting SAR images into multiple classes. The proposed CNN is specifically designed to run on remote embedded systems, which have very limited hardware capability and strict limits on power consumption. Even if the performance in terms of results accuracy does not represent a step forward compared with previous solutions, the presented CNN has the important advantage of being able to run on remote embedded systems with limited hardware resources while achieving good performance. The presented CNN is compatible with dedicated hardware accelerators available on the market due to its low memory footprint and small size. It also provides many additional very significant advantages, such as having shorter inference times, requiring shorter training times, and avoiding transmission of irrelevant data. Our goal is to allow embedded low power remote devices such as satellite systems for remote sensing to be able to directly run CNNs on board, so that the amount of data that needs to be transmitted to ground and processed on ground can be substantially reduced, which will be greatly beneficial in significantly reducing the amount of time needed for identification of oil spills from SAR images.

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A Deep-Learning Framework for the Detection of Oil Spills from SAR Data

Cited by 63 publications

References 31 publications

Resting-state electroencephalography based deep-learning for the detection of Parkinson’s disease

Resting-state electroencephalography based deep-learning for the detection of Parkinson’s disease

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

Oil Spill Identification from SAR Images for Low Power Embedded Systems Using CNN

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