Sea Ice Classification via Deep Neural Network Semantic Segmentation

2020 ◽  
pp. 1-1
Author(s):  
Benjamin Dowden ◽  
Oscar De Silva ◽  
Weimin Huang ◽  
Dan Oldford
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Deep Neural Network ◽  
Semantic Segmentation

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

scholarly journals A Deep Neural Network for Oil Spill Semantic Segmentation in Sar Images

2018 ◽  
Author(s):  
Georgios Orfanidis ◽  
Konstantinos Ioannidis ◽  
Konstantinos Avgerinakis ◽  
Stefanos Vrochidis ◽  
Ioannis Kompatsiaris
Keyword(s):  
Neural Network ◽  
Oil Spill ◽  
Deep Neural Network ◽  
Semantic Segmentation ◽  
Sar Images

Image Scene Analysis Based on Improved FCN Model

2021 ◽  
pp. 2152020
Author(s):  
Weijie Yang ◽  
Yueting Hui
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Scene Analysis ◽  
Model Structure ◽  
Multi Scale ◽  
Class Labels ◽  
Small Targets ◽  
Scene Content

Image scene analysis is to analyze image scene content through image semantic segmentation, which can identify the categories and positions of different objects in an image. However, due to the loss of spatial detail information, the accuracy of image scene analysis is often affected, resulting in rough edges of FCN, inconsistent class labels of target regions and missing small targets. To address these problems, this paper increases the receptive field, conducts multi-scale fusion and changes the weight of different sensitive channels, so as to improve the feature discrimination and maintain or restore spatial detail information. Furthermore, the deep neural network FCN is used to build the base model of semantic segmentation. The ASPP, data augmentation, SENet, decoder and global pooling are added to the baseline to optimize the model structure and improve the effect of semantic segmentation. Finally, the more accurate results of scene analysis are obtained.

scholarly journals DETECTION AND MONITORING OF BEACH LITTER USING UAV IMAGE AND DEEP NEURAL NETWORK

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 55-58 ◽  
Author(s):  
S. H. Bak ◽  
D. H. Hwang ◽  
H. M. Kim ◽  
H. J. Yoon
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Monitoring Process ◽  
Neural Network Algorithm ◽  
Beach Litter ◽  
Trained Neural Network ◽  
Uav Image

<p><strong>Abstract.</strong> Beach litter destroys marine ecosystems and creates aesthetic discomfort that lowers the value of the beach. In order to solve this beach litter problem, it is necessary to study the generation and distribution pattern of waste and the cause of the inflow. However, the data for the study are only sample data collected in some areas of the beach. Also, most of the data covers only the total amount of beach litter. UAV(Unmanned Aerial Vehicle) and Deep Neural Network can be effectively used to detect and monitor beach litter. Using UAV, it is possible to easily survey the entire beach. The Deep Neural Network can also identify the type of coastal litter. Therefore, using UAV and Deep Neural Network, it is possible to acquire spatial information by type of beach litter.</p> <p>This paper proposes a Beach litter detection algorithm based on UAV and Deep Neural Network and a Beach litter monitoring process using it. It also offers optimal shooting altitude and film duplication to detect small beach litter such as plastic bottles and styrofoam pieces found on the beach.</p> <p>In this study, DJI Mavic 2 Pro was used. The camera on the UAV is a 1-inch CMOS with a resolution of 20MP. The images obtained through UAV are produced as orthoimages and input into a pre-trained neural network algorithm. The Deep Neural Network used for Beach litter detection removed the Fully Connected Layer from the Convolutional Neural Network for semantic segmentation.</p>

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