Oil Spills Identification in SAR Image Using mRMR and SVM Model

2018 ◽  
Author(s):  
Hui Zhou ◽  
Chen Peng
Keyword(s):  
Oil Spills ◽  
Sar Image ◽  
Svm Model

scholarly journals SAR Oil Spill Detection System through Random Forest Classifiers

2021 ◽  
Vol 13 (11) ◽  
pp. 2044
Author(s):  
Marcos R. A. Conceição ◽  
Luis F. F. Mendonça ◽  
Carlos A. D. Lentini ◽  
André T. C. Lima ◽  
José M. Lopes ◽  
...  
Keyword(s):  
Random Forest ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection System ◽  
Feature Space ◽  
Dark Spot ◽  
Sar Image ◽  
Extensive Search ◽  
Gradient Based ◽  
Biological Films

A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were oil spills, biological films, rain cells, low wind regions, clean sea surface, ships, and terrain. The second one was a SAR image oil detector named “Radar Image Oil Spill Seeker (RIOSS)”, which classified oil-like targets. An optimized feature space to serve as input to such classification models, both in terms of variance and computational efficiency, was developed. It involved an extensive search from 42 image attribute definitions based on their correlations and classifier-based importance estimative. This number included statistics, shape, fractal geometry, texture, and gradient-based attributes. Mixed adaptive thresholding was performed to calculate some of the features studied, returning consistent dark spot segmentation results. The selected attributes were also related to the imaged phenomena’s physical aspects. This process helped us apply the attributes to a random forest, increasing our algorithm’s accuracy up to 90% and its ability to generate even more reliable results.

scholarly journals Feature Merged Network for Oil Spill Detection using SAR Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3174
Author(s):  
Yonglei Fan ◽  
Xiaoping Rui ◽  
Guangyuan Zhang ◽  
Tian Yu ◽  
Xijie Xu ◽  
...  
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Recognition Accuracy ◽  
Original Model ◽  
High Dimensional ◽  
Sar Image ◽  
Sar Images ◽  
Marine Oil ◽  
Marine Oil Spills ◽  
Overfitting Problem

The frequency of marine oil spills has increased in recent years. The growing exploitation of marine oil and continuous increase in marine crude oil transportation has caused tremendous damage to the marine ecological environment. Using synthetic aperture radar (SAR) images to monitor marine oil spills can help control the spread of oil spill pollution over time and reduce the economic losses and environmental pollution caused by such spills. However, it is a significant challenge to distinguish between oil-spilled areas and oil-spill-like in SAR images. Semantic segmentation models based on deep learning have been used in this field to address this issue. In addition, this study is dedicated to improving the accuracy of the U-Shape Network (UNet) model in identifying oil spill areas and oil-spill-like areas and alleviating the overfitting problem of the model; a feature merge network (FMNet) is proposed for image segmentation. The global features of SAR image, which are high-frequency component in the frequency domain and represents the boundary between categories, are obtained by a threshold segmentation method. This can weaken the impact of spot noise in SAR image. Then high-dimensional features are extracted from the threshold segmentation results using convolution operation. These features are superimposed with to the down sampling and combined with the high-dimensional features of original image. The proposed model obtains more features, which allows the model to make more accurate decisions. The overall accuracy of the proposed method increased by 1.82% and reached 61.90% compared with the UNet. The recognition accuracy of oil spill areas and oil-spill-like areas increased by approximately 3% and reached 56.33%. The method proposed in this paper not only improves the recognition accuracy of the original model, but also alleviates the overfitting problem of the original model and provides a more effective monitoring method for marine oil spill monitoring. More importantly, the proposed method provides a design principle that opens up new development ideas for the optimization of other deep learning network models.

Segmentation of oil spills in SAR image based on improved CV model

2015 ◽  
Author(s):  
Wenjie Tie Wenjie Tie ◽  
Chonglei Wang Chonglei Wang ◽  
Fukun Bi Fukun Bi ◽  
Hao Shi Hao Shi ◽  
Xu Zhang Xu Zhang ◽  
...  
Keyword(s):  
Oil Spills ◽  
Sar Image ◽  
Cv Model

Oil Spills Identification in SAR Image Using Mahalanobis Distance

2012 ◽  
Vol 466-467 ◽  
pp. 246-250
Author(s):  
Peng Chen ◽  
Hui Zhou ◽  
Xiao Tian Wang
Keyword(s):  
High Resolution ◽  
Mahalanobis Distance ◽  
Oil Spills ◽  
Feature Vector ◽  
Weather Conditions ◽  
Target Object ◽  
The Other ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Accuracy Rate

This paper presents a method of oil spills identification in Synthetic Aperture Radar (SAR) image based on feature vector, it makes use of the advantages of SAR which can work on day and night and all weather conditions with high resolution monitoring for oil spills. Use the algorithm of Mahalanobis distance to identify the target object and gain the feature vector through evaluating SAR image of the dark area boundary. It is proved by experiment that the number of selected feature value is reasonable and more effective for estimating whether has oil spills than the traditional one. The accuracy rate can reach 96% or even more for using the algorithm of Mahalanobis distance and compare to the other methods of oil spills identification it is easy for programming implementation with less conditions .

scholarly journals Oil Spill Identification from Satellite Images Using Deep Neural Networks

2019 ◽  
Vol 11 (15) ◽  
pp. 1762 ◽  
Author(s):  
Marios Krestenitis ◽  
Georgios Orfanidis ◽  
Konstantinos Ioannidis ◽  
Konstantinos Avgerinakis ◽  
Stefanos Vrochidis ◽  
...  
Keyword(s):  
Neural Networks ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection Methods ◽  
Future Research ◽  
Complex Nature ◽  
Sar Image ◽  
Custom Made ◽  
Oil Spill Identification ◽  
Segmentation Models

Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.

Marine Oil Spill Detection in SAR Image Based on Mathematical Morphology

2012 ◽  
Vol 256-259 ◽  
pp. 2320-2323
Author(s):  
Liang Liu ◽  
Xin Guo Cui ◽  
Ming Jiu Chen ◽  
Ying Jun Sun
Keyword(s):  
Image Segmentation ◽  
Mathematical Morphology ◽  
Oil Spill ◽  
Marine Environment ◽  
Oil Spills ◽  
Speckle Noise ◽  
Histogram Equalization ◽  
Sar Image ◽  
Marine Oil ◽  
Envisat Asar

Oil spills are almost constantly present in recent years, which cause ecological disaster of marine environment. It is important to detect the area of oil spill to take rescue measurement in time. The paper took the EnviSat Asar image as example to detect the boundary of oil spills. Firstly, the paper removed speckle noise by Histogram Equalization. Then, image segmentation was carried out by open-close mixed operation in mathematical morphology. At last, the region of oil spill was exacted independently.

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