Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms

Cantorna, Diego; Dafonte, Carlos; Iglesias, Alfonso; Arcay, B.

doi:10.1016/j.asoc.2019.105716

Cited by 73 publications

(40 citation statements)

References 28 publications

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“…In the existing literature, single-pol SAR image semantic segmentation focuses on the segmentation of a single class of land cover, such as oil spill segmentation [3], building segmentation [11], and road segmentation [12]. Semantic segmentation of single-pol SAR images for multiple types of terrain categories This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: A Motivation and Objectivementioning

confidence: 99%

“…Even with the improvement of SAR image resolution, it is not tailored for semantic segmentation, where individual terrain objects can be well segmented. To achieve object-level land cover classification with high-resolution SAR images, which is known as image semantic segmentation in computer vision, deep learning-based segmentation methods have been widely studied [3], [10]- [12].…”

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confidence: 99%

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Object-Level Semantic Segmentation on the High-Resolution Gaofen-3 FUSAR-Map Dataset

Shi

Chen

et al. 2021

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

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Land cover classification with SAR images mainly focuses on the utilization of fully polarimetric SAR (PolSAR) images. The conventional task of PolSAR classification is single-pixel-based region-level classification using polarimetric target decomposition. In recent years, a large number of high-resolution SAR images have become available, most of which are single-polarization. This article explores the potential of object-level semantic segmentation of high-resolution single-pol SAR images, in particular tailored for the Gaofen-3 (GF-3) sensor. First, a well-annotated GF-3 segmentation dataset "FUSAR-Map" is presented for SAR semantic segmentation. It is based on four data sources: GF-3 single-pol SAR images, Google Earth optical remote sensing images, Google Earth digital maps, and building footprint vector data. It consists of 610 high-resolution GF-3 single-pol SAR images with the size of 1024 × 1024. Second, an encoder-decoder network based on transfer learning is employed to implement semantic segmentation of GF-3 SAR images. For the FUSAR-Map dataset, an optical image pretrained deep convolution neural network (DCNN) is fine-tuned with the SAR training dataset. Experiments on the FUSAR-Map dataset demonstrate the feasibility of object-level semantic segmentation with high-resolution GF-3 single-pol SAR images. Also, our algorithm obtains fourth place about the PolSAR image semantic segmentation on the "2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation." The new dataset and the encoder-decoder network are intended as the benchmark data and baseline algorithm for further development of semantic segmentation with high-resolution SAR images. The FUSAR-Map and our algorithm are available at github.com/fudanxu/FUSAR-Map/.

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Section: A Motivation and Objectivementioning

confidence: 99%

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confidence: 99%

Object-Level Semantic Segmentation on the High-Resolution Gaofen-3 FUSAR-Map Dataset

Shi

Chen

et al. 2021

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

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“…In another example, a CNN regression was proposed to develop a model applicable to hyperspectral imagery for estimation of concentrations of phycocyanin and chlorophyll-a [52]. CNNs also have been used in OpenStreetMap Data Quality Assessment [53], oil spill segmentation [54], ship position detection and direction prediction [55], multimodal RS image registration [56], road extraction [57], and many other areas of study [58][59][60].…”

Section: Introductionmentioning

confidence: 99%

A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

Ghanbari

Mahdianpari

Homayouni

et al. 2021

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

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Since the rise of deep learning in the past few years, convolutional neural networks (CNNs) have quickly found their place within the remote sensing (RS) community. As a result, they have transitioned away from other machine learning techniques, achieving unprecedented improvements in many specific RS applications. This paper presents a meta-analysis of 416 peer-reviewed journal articles, summarizes CNN advancements, and its current status under RS applications. The review process includes a statistical and descriptive analysis of a database comprised of 23 fields, including 1) general characteristics such as various applications, study objectives, sensors, and data types, and 2) algorithm specifications such as different types of CNN models, parameter settings, and reported accuracies. This review begins with a comprehensive survey of the relevant articles without considering any specific criteria to give readers an idea of general trends, then investigates CNNs within different RS applications to provide specific directions for the researchers. Finally, a conclusion summarizes potentialities, critical issues, and challenges related to the observed trends.

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“…Other methods employ various sensors such as visible sensors, infrared sensors, ultraviolet sensors, radar sensors, and laser fluorosensor. A lot of emphasis has been placed on sea oil pollution and the use of synthetic aperture radar (SAR) imaging by satellites for remote sensing, mapping and monitoring of marine oil spills [11][12][13][14]. The detection of oil spills using SAR images are divided into stages: pre-processing of the images, segmentation, feature extraction; oil spill and lookalike discrimination; and classification of dark spots which potentially correspond to oil spills [11,13,[15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Results of the Crude Oil Filter CrossCorrelation Algorithm on 50 documented crude oil spill images from SPDC[11].…”

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confidence: 99%

Oil spill identification in visible sensor imaging using automated cross correlation with crude oil image filters

Ofualagba

Onyishi

2020

Nig. J. Tech.

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An algorithm for detection of crude oil spills in visible light images has been developed and tested on 50 documented crude oil spill images from Shell Petroleum Development Company (SPDC) Nigeria. A set of three 25 x 25 pixels crude oil filters, with unique red, green, and blue (RGB) colour values, homogeneity, and power spectrum density (PSD) features were cross-correlated with the documented spill images. The final crude oil spill Region of Interest (ROI) was determined by grouping interconnected pixels based on their proximity, and only selecting ROIs with an area greater than 5,000 pixels. The crude oil filter cross correlation algorithm demonstrated a sensitivity of 84% with a False Positive per Image (FPI) of 0.82. Future work includes volume estimation of detected spills using crude oil filters, and utilizing this information in the recommendation of appropriate spill clean-up and remediation procedures for the detected spills. Keywords: Crude Oil Spill Detection, Crude oil image filters, Cross correlation, Visible sensor imaging, Oil Spill Segmentation.

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Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms

Cited by 73 publications

References 28 publications

Object-Level Semantic Segmentation on the High-Resolution Gaofen-3 FUSAR-Map Dataset

Object-Level Semantic Segmentation on the High-Resolution Gaofen-3 FUSAR-Map Dataset

A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

Oil spill identification in visible sensor imaging using automated cross correlation with crude oil image filters

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