Research on SAR oil spill image classification based on DBN in small sample space

Chen, Guilian; Guo, Hao; J, An

doi:10.1109/icsai.2017.8248340

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…CNNs, followed by AEs, are commonly used DL models for identifying oil spills from remotely sensed data; however, only a few studies have adopted other DL models, such as DBN, RNN, and generative adversarial network (GAN). Chen and Guo [195] proposed a DBN model to distinguish oil spills, lookalikes, and water in three SAR images from a small sample space database. Chen et al [103] analyzed and compared the performance of SAE, DBN, and several classical algorithms to identify the presence of oil spills from a limited number of samples.…”

Section: Other Deep Learning Modelsmentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

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Section: Other Deep Learning Modelsmentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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“…Experimental results show that deep learning has achieved good results in all these fields. Thus, deep learning methods such as the convolutional neural network (CNN) [12], convolutional auto-encoder network (CAE) [13], sparse coding [14] and deep belief network (DBN) [15] have been continuously tapped and explored by researchers, where CNN is the most widely used. Traditional CNN structure includes convolutional layer with the stride of one, pooling layer, and fully connected layer.…”

Section: Introductionmentioning

confidence: 99%

SAR image classification based on multi‐feature fusion decision convolutional neural network

Guo

2021

IET Image Processing

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With the success of convolutional neural network (CNN) in the field of image processing and image recognition, an improved fully convolutional neural network (FCNN) based on CNN is proposed. Although FCNN has better performance than CNN, the number of parameters of FCNN has also increased significantly compared to CNN. Consequently, this paper proposes a multi-feature fusion decision convolutional neural network (MFFD-CNN), which use two kinds down-sample methods in the pooling layer, and chooses the concatenate or add operation as the way that consolidates the feature of the convolutional layer with a stride of 2 and the max-pooling layer to execute fusion operations. Meanwhile replaces the traditional softmax loss with an additive margin Softmax (AM-Softmax) loss. The structure, which is based on the multi-target classification experiment results of the MSTAR data set, shows can not only obtain an average correct recognition rate 1.2% higher than FCNN, but also better stability without augmenting the training samples of MSTAR data.

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Recent Advances in SAR Image Analysis Using Deep Learning Approaches: Examples of Speckle Denoising and Change Detection

Imad,

Sara,

Hajji

et al. 2024

2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)

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Research on SAR oil spill image classification based on DBN in small sample space

Cited by 3 publications

References 9 publications

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

SAR image classification based on multi‐feature fusion decision convolutional neural network

Recent Advances in SAR Image Analysis Using Deep Learning Approaches: Examples of Speckle Denoising and Change Detection

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