Oil spills detection from SAR Earth observations based on a hybrid CNN transformer networks

Dehghani-Dehcheshmeh, Saeid; Akhoondzadeh, Mehdi; Homayouni, Saeid

doi:10.1016/j.marpolbul.2023.114834

Cited by 12 publications

(3 citation statements)

References 34 publications

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Section: Field Of Application Applicationmentioning

confidence: 99%

“…Agriculture Crop type mapping [83] Rice yield prediction [34] Downy mildew disease detection [33] Mariculture cage segmentation [84] Environment protection Smoke-like scenes classification [85] Building damage assessment [86] Detection of melt ponds on sea ice [87] Oil spills detection [88] Deforestation monitoring [89] Snowmelt flood susceptibility assessment [90] Tailings ponds detection [82] Mapping Wetland mapping [45] Urban planning Urban building classification [43,91] UIS classification [23,92] Others Small object detection [58] Ship detection [93] RS image captioning [94] Due to the continuous exploration of transformers by researchers, they have been applied quite successfully in different scenarios of RS, playing different roles. However, we found that the current research of transformers in RS is only limited to observation related to the environment, whether it is the natural environment or urban environment.…”

Section: Field Of Application Applicationmentioning

confidence: 99%

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Transformers for Remote Sensing: A Systematic Review and Analysis

Wang,

Ma,

et al. 2024

Sensors

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Research on transformers in remote sensing (RS), which started to increase after 2021, is facing the problem of a relative lack of review. To understand the trends of transformers in RS, we undertook a quantitative analysis of the major research on transformers over the past two years by dividing the application of transformers into eight domains: land use/land cover (LULC) classification, segmentation, fusion, change detection, object detection, object recognition, registration, and others. Quantitative results show that transformers achieve a higher accuracy in LULC classification and fusion, with more stable performance in segmentation and object detection. Combining the analysis results on LULC classification and segmentation, we have found that transformers need more parameters than convolutional neural networks (CNNs). Additionally, further research is also needed regarding inference speed to improve transformers’ performance. It was determined that the most common application scenes for transformers in our database are urban, farmland, and water bodies. We also found that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development.

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Section: Field Of Application Applicationmentioning

confidence: 99%

Section: Field Of Application Applicationmentioning

confidence: 99%

Transformers for Remote Sensing: A Systematic Review and Analysis

Wang,

Ma,

et al. 2024

Sensors

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“…Seyd Teymoor Seydi et al utilized a one-dimensional multiscale residual convolutional neural network to classify pixels based on the spectral features of oil spill region pixels, aiming to achieve the detection of oil spill areas [ 16 ]. Saeid Dehghani-Dehcheshmeh et al combined CNN and Visual Transformer (ViT) methodologies to segment images into two categories: oil spill and background, effectively achieving oil spill detection [ 17 ]. The aforementioned methods have made significant advancements in the detection of oil spill areas in marine environments, underscoring the immense potential of hyperspectral imaging in this domain.…”

Section: Introductionmentioning

confidence: 99%

Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging

Zhan,

Bai,

et al. 2024

Sensors

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Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models—DRSNet, CNN-Visual Transformer, and GCN—conducting a comprehensive analysis to evaluate the advantages and limitations of each model.

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