Automated Marine Oil Spill Detection Using Deep Learning Instance Segmentation Model

Yekeen, Shamsudeen Temitope; Balogun, Abdul‐Lateef

doi:10.5194/isprs-archives-xliii-b3-2020-1271-2020

Cited by 16 publications

(9 citation statements)

References 26 publications

(24 reference statements)

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“…Recent deep-learning-based methods [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] that used CNN structures for both automated feature extraction, as well as classification of SAR images have relied on the use of patches to reduce the background concentration in the tested images. Pre-trained models, such as ResNet 101, VGG-16, and GAN networks as in [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] or multi-level CNN networks as in [9], were introduced to classify patches with modest performance (i.e., precision and Dice score). In [23], authors obtained improved results via introducing the use of VGG-16 and dark batch generation algorithm on spaceborne SAR images.…”

Section: Discussionmentioning

confidence: 99%

“…Yekeen et al [24,25] introduced the use of mask-region-based CNN to distinguish between ships, oil spills, and look-alikes where pre-trained ResNet 101 and feature pyramid network were used for feature extraction, regional proposal network was deployed for the region of interest extraction, and the mask-region-based CNN was used for semantic segmentation. The proposed model introduced a classification accuracy of 96%, and 92% for oil spills and look-alikes, respectively.…”

Section: Related Workmentioning

confidence: 99%

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

Shaban

Salim

Khalifeh

et al. 2021

Sensors

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Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Shaban

Salim

Khalifeh

et al. 2021

Sensors

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“…CNNs have been widely used to perform segmentation of SAR images to identify oil spills [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. One example of this approach can be found in [26], where a NN, specifically the Multilayer Perceptron (MLP), was applied to SAR images for the first time.…”

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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“…This is because it supports both pixel-level classification and detects object categories independently. CNN's higher accuracy in object recognition, detection, and segmentation as well as its ability to localize the instance under consideration which enables oil spill detection in complex scenarios (e.g., where oil spill and lookalike overlay each other) have been highlighted [169,202]. The CNN could be applied for both semantic and instance segmentation.…”

Section: Automatic Oil Spill Detectionmentioning

confidence: 99%

“…However, limitations exist in the application of instance segmentation algorithms to oil spill detection. Nonetheless, recent studies suggest that considering other features in the sea (e.g., ship, water body, and land area) can enhance detection accuracy [169,202].…”

Section: Automatic Oil Spill Detectionmentioning

confidence: 99%

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

Yekeen

Balogun

2020

Remote Sensing

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Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

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Automated Marine Oil Spill Detection Using Deep Learning Instance Segmentation Model

Cited by 16 publications

References 26 publications

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

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

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

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

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