A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

Liu, Bingxin; Li, Ying; Li, Guannan; Liu, Anling

doi:10.3390/ijgi8040160

Cited by 57 publications

(27 citation statements)

References 52 publications

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“…Although the first U-Net models were introduced in 2015, the first models for land-cover classification have been published since 2019, such as land covers in urban [35]- [37], forest [38], coastal areas [39] and sea surface. Most of studies developed the original U-Net with the use of secondary satellite images [40]. For example, A. Stoian et al [41] have started to use 10m-resolution Sentinel-2 satellite images for land use/cover mapping in many polygons in France.…”

Section: Introductionmentioning

confidence: 99%

U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery

Giang¹,

Dang

et al. 2020

IEEE Access

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Mining activities are the leading cause of deforestation, land-use changes, and pollution. Land use/cover mapping in Vietnam every five years is not useful to monitor land covers in mining areas, especially in the Central Highland region. It is necessary to equip managers with a better tool to monitor and map land cover using high-resolution images. Therefore, the authors proposed using the U-Net convolutional network for land-cover classification based on multispectral Unmanned aerial vehicle (UAV) image in a mining area of Daknong province, Vietnam. An area of 0.5kmx0.8km was used for training and testing seven U-Net models using seven optimizer function types. The final U-Net model can interpret six land cover types: (1) open-case mining lands, (2) old permanent croplands, (3) young permanent croplands, (4) grasslands, (5) bare soils, (6) water bodies. As a result, two models using Nadam and Adadelta optimizer function can be used to classify six land cover types with accuracy higher than 83%, especially in open-case mining lands and polluted streams flowed out from the mining areas. The trained U-Net models can potentially update new land cover types in other mining areas towards monitoring land cover changes in real-time in the future.

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Section: Introductionmentioning

confidence: 99%

U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery

Giang¹,

Dang

et al. 2020

IEEE Access

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“…We believe that the algorithm of two kinds of classifiers is the main reason that the classification accuracy of the same tree species differs greatly. For deep convolutional neural networks, each neuron is no longer connected with all neurons in the upper layer but only some of them [23,40,64]. Furthermore, the use of an activation function increases the nonlinearity of the neural network [24].…”

Section: Application Of Deep Learning In Tree Species Identificationmentioning

confidence: 99%

“…Furthermore, the use of an activation function increases the nonlinearity of the neural network [24]. Compared with other functions [65], the ReLU function can effectively alleviate the problem of overfitting and increase the accuracy of tree species classification [66], so the convolutional neural network can achieve better learning by keeping as many important parameters as possible and removing a large number of unimportant parameters [64,67]. Although random forest has strong generalization ability, it is easy to overfit the training data with large noise [39,54,68].…”

Section: Application Of Deep Learning In Tree Species Identificationmentioning

confidence: 99%

Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China

Ren

Wang

et al. 2019

Forests

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The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future.

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“…Therefore, this network can provide a more precise prediction than former remote sensing computing strategies such as unsupervised learning, Random Forest [30,31], pixel-based, and Support Vector Machine [32][33][34]. In the recent three years, various upgraded NN networks for standard land-cover classification were proposed, such as Convolutional Neural Network (CNN) [33,35,36], R-CNN, U-Net, and Mask-RCNN [35,37,38]. For the coastal wetland classification, these deep-learning-based models using both spatial and spectral data are considered a potential end-to-end solution to separate objects affected by water.…”

Section: Introductionmentioning

confidence: 99%

Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam

Dang

Nguyen

Nguyễn³

et al. 2020

Remote Sensing

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The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time.

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A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

Cited by 57 publications

References 52 publications

U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery

U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery

Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China

Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam

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