Query: Sea ice deep learning

title: Supplementing Remote Sensing of Ice: Deep Learning Based Image Segmentation System for Automatic Detection and Localization of Sea ice Formations From Close Range Optical Images
abstract: This paper presents a three stage approach for the automated analysis of close range optical images containing ice objects. The proposed system is based on an ensemble of deep learning models and conditional random field postprocessing. The following surface ice formations were considered: Icebergs, Deformed ice, Level ice, Broken ice, Ice floes, Floebergs, Floebits, Pancake ice, and Brash ice. Additionally, five non surface ice categories were considered: Sky, Open water, Shore, Underwater ice, and Melt ponds. To find input parameters for the approach, the performance of 12 different neural network architectures was explored and evaluated using a 5 fold cross validation scheme. The best performance was achieved using an ensemble of models having pyramid pooling layers (PSPNet, PSPDenseNet, DeepLabV3+ and UPerNet) and convolutional conditional random field postprocessing with a mean intersection over union score of 0.799, and this outperformed the best single model approach. The results of this study show that when per class performance was considered, the Sky was the easiest class to predict, followed by Deformed ice and Open water. Melt pond was the most challenging class to predict. Furthermore, we have extensively explored the strengths and weaknesses of our approach and, in the process, discovered the types of scenes that pose a more significant challenge to the underlying neural networks. When coupled with optical sensors and AIS, the proposed approach can serve as a supplementary source of large scale 'ground truth' data for validation of satellite based sea ice products. We have provided an implementation of the approach at https:/github.com/panchinabil/sea_ice_segmentation.

title: Arctic sea ice cover data from spaceborne SAR by deep learning
abstract: Widely used sea ice concentration and sea ice cover in polar regions are derived mainly from spaceborne microwave radiometer and scatterometer data, and the typical spatial resolution of these products ranges from several to dozens of kilometers. Due to dramatic changes in polar sea ice, high resolution sea ice cover data are drawing increasing attention for polar navigation, environmental research, and offshore operations. In this paper, we focused on developing an approach for deriving a high resolution sea ice cover product for the Arctic using Sentinel 1 (S1) dual polarization (horizontal horizontal, HH, and horizontal vertical, HV) data in extra wide swath (EW) mode. The approach for discriminating sea ice from open water by synthetic aperture radar (SAR) data is based on a modified U Net architecture, a deep learning network. By employing an integrated stacking model to combine multiple U Net classifiers with diverse specializations, sea ice segmentation is achieved with superior accuracy over any individual classifier. We applied the proposed approach to over 28,000 S1 EW images acquired in 2019 to obtain sea ice cover products in a high spatial resolution of 400 m. By converting the S1 derived sea ice cover to concentration and then compared with Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration data, showing an average absolute difference of 5.55 with seasonal fluctuations. A direct comparison with Interactive Multisensor Snow and Ice Mapping System (IMS) daily sea ice cover data achieves an average accuracy of 93.98 These results show that the developed S1 derived sea ice cover results are comparable to the AMSR and IMS data in terms of overall accuracy but superior to these data in presenting detailed sea ice cover information, particularly in the marginal ice zone (MIZ) Data are available at: https:/doi.org/10.11922/sciencedb.00273 (Wang and Li, 2020)

title: Sea Ice and Open Water Classification of SAR Images Using a Deep Learning Model
abstract: Accurate and robust classification methods of sea ice and open water are significant for many applications. Synthetic Aperture Radar (SAR) imaging capability is independent of weather conditions and is widely used in sea ice classification. U Net, a deep learning framework, has achieved great success in the field of biomedical image classification. In this study, we construct a U Net based "end to end" model to classify the sea ice and open water pixels in SAR imagery. Five SAR images acquired in the Gulf of Alaska near Bering Strait are used in this case study. We manually label the SAR images as ice and water. The labeled images from the first four SAR image are divided into chips to be fed into the U Net model for training. The fifth SAR image is employed as the testing data. Experiments show that the precision and the recall of the testing image is 91.64% and 91.70% respectively. Most of the sea ice, including small chunks and sinuous ice edges, can be successfully classified.

title: Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning
abstract: Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT 2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadian territory. This study serves as a proof of concept that neural networks can be used to accurately predict ice type from SAR data. Datasets of SAR images served as inputs, and CIS ice charts served as labelled outputs to train a neural network to classify sea ice type. Our results show that DenseNet achieves the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input samples. The 91.8% ice classification accuracy validates the premise that a neural network can be used to effectively categorize different ice types based on SAR data.

title: Transferred Deep Learning for Sea Ice Change Detection From Synthetic Aperture Radar Images
abstract: High quality sea ice monitoring is crucial to navigation safety and climate research in the polar regions. In this letter, a transferred multilevel fusion network (MLFN) is proposed for sea ice change detection from synthetic aperture radar (SAR) images. Considering the fact that training data are limited in the task of sea ice change detection, a large data set was used to train the MLFN, and the deep knowledge can be transferred to sea ice analysis. In addition, cascade dense blocks are employed to optimize the convolutional layers. Multilayer feature fusion is introduced to exploit the complementary information among low mid and high level feature representations. Therefore, more discriminative feature extraction can be achieved by the MLFN. Furthermore, the fine tune strategy is utilized to optimize the network parameters. The experimental results on two real sea ice data sets demonstrated that the proposed method achieved better performance than other competitive methods.

title: Deep learning for monthly Arctic sea ice concentration prediction
abstract: <p>Over recent decades, the Arctic has warmed faster than any region on Earth. The rapid decline in Arctic sea ice extent (SIE) is often highlighted as a key indicator of anthropogenic climate change. Changes in sea ice disrupt Arctic wildlife and indigenous communities, and influence weather patterns as far as the mid latitudes. Furthermore, melting sea ice attenuates the albedo effect by replacing the white, reflective ice with dark, heat absorbing melt ponds and open sea, increasing the Sun&#8217;s radiative heat input to the Arctic and amplifying global warming through a positive feedback loop. Thus, the reliable prediction of sea ice under a changing climate is of both regional and global importance. However, Arctic sea ice presents severe modelling challenges due to its complex coupled interactions with the ocean and atmosphere, leading to high levels of uncertainty in numerical sea ice forecasts./p><p>Deep learning (a subset of machine learning) is a family of algorithms that use multiple nonlinear processing layers to extract increasingly high level features from raw input data. Recent advances in deep learning techniques have enabled widespread success in diverse areas where significant volumes of data are available, such as image recognition, genetics, and online recommendation systems. Despite this success, and the presence of large climate datasets, applications of deep learning in climate science have been scarce until recent years. For example, few studies have posed the prediction of Arctic sea ice in a deep learning framework. We investigate the potential of a fully data driven, neural network sea ice prediction system based on satellite observations of the Arctic. In particular, we use inputs of monthly averaged sea ice concentration (SIC) maps since 1979 from the National Snow and Ice Data Centre, as well as climatological variables (such as surface pressure and temperature) from the European Centre for Medium Range Weather Forecasts reanalysis (ERA5) dataset. Past deep learning based Arctic sea ice prediction systems tend to overestimate sea ice in recent years we investigate the potential to learn the non stationarity induced by climate change with the inclusion of multi decade global warming indicators (such as average Arctic air temperature) We train the networks to predict SIC maps one month into the future, evaluating network prediction uncertainty by ensembling independent networks with different random weight initialisations. Our model accounts for seasonal variations in the drivers of sea ice by controlling for the month of the year being predicted. We benchmark our prediction system against persistence, linear extrapolation and autoregressive models, as well as September minimum SIE predictions from submissions to the Sea Ice Prediction Network's Sea Ice Outlook. Performance is evaluated quantitatively using the root mean square error and qualitatively by analysing maps of prediction error and uncertainty./p>

title: Hyperspectral Sea Ice Image Classification Based on the Spectral Spatial Joint Feature with Deep Learning
abstract: Sea ice is one of the causes of marine disasters. The classification of sea ice images is an important part of sea ice detection. The labeled samples in hyperspectral sea ice image classification are difficult to acquire, which causes minor sample problems. In addition, most of the current sea ice classification methods mainly use spectral features for shallow learning, which also limits further improvement of the sea ice classification accuracy. Therefore, this paper proposes a hyperspectral sea ice image classification method based on the spectral spatial joint feature with deep learning. The proposed method first extracts sea ice texture information by the gray level co occurrence matrix (GLCM) Then, it performs dimensionality reduction and a correlation analysis of the spectral information and spatial information of the unlabeled samples, respectively. It eliminates redundant information by extracting the spectral spatial information of the neighboring unlabeled samples of the labeled sample and integrating the information with the spectral and texture data of the labeled sample to further enhance the quality of the labeled sample. Lastly, the three dimensional convolutional neural network (3D CNN) model is designed to extract the deep spectral spatial features of sea ice. The proposed method combines relevant textural features and performs spectral spatial feature extraction based on the 3D CNN model by using a large amount of unlabeled sample information. In order to verify the effectiveness of the proposed method, sea ice classification experiments are carried out on two hyperspectral data sets: Baffin Bay and Bohai Bay. Compared with the CNN algorithm based on a single feature (spectral or spatial) and other CNN algorithms based on spectral spatial features, the experimental results show that the proposed method achieves better sea ice classification (98.52% and 97.91% with small samples. Therefore, it is more suitable for classifying hyperspectral sea ice images.

title: Deep learning based retrieval algorithm for Arctic sea ice concentration from AMSR2 passive microwave and MODIS optical data
abstract: This study applies deep learning (DL) to retrieve Arctic sea ice concentration (SIC) from AMSR2 data. MODIS derived SICs are calculated based on spectral unmixing with a new ice/water endmember extraction algorithm that exploits global/local representatives, and then used to train a DL network with AMSR2 data. The resulting SIC maps outperform popular SIC products both regionally and globally. The RMSE of the proposed DL model is 5.19, whereas those of the widely used Bootstrap and ASI based SIC images are 6.54 and 7.38, respectively, with respect to MODIS derived SICs at global scale. In particular, our proposed method better describes regions of low SIC and melting ice in summer, which are generally difficult to estimate. As the DL based model consistently generates accurate SIC values that are not time or region dependent, it is considered to be an operational system. Additionally, our SICs can be used to generate initial conditions facilitating development of more accurate climate models.

title: A Study on Classifying Sea Ice of the Summer Arctic Ocean Using Sentinel 1 A/B SAR Data and Deep Learning Models
abstract: The importance of high resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study, sea ice classification algorithms for two deep learning models were examined using Sentinel1 A/B SAR data to generate high resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66% and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

title: Sea Ice Image Classification Based on Heterogeneous Data Fusion and Deep Learning
abstract: Sea ice is one of the typical causes of marine disasters. Sea ice image classification is an important component of sea ice detection. Optical data contain rich spectral information, but they do not allow one to easily distinguish between ground objects with a similar spectrum and foreign objects with the same spectrum. Synthetic aperture radar (SAR) data contain rich texture information, but the data usually have a single source. The limitation of single source data is that they do not allow for further improvements of the accuracy of remote sensing sea ice classification. In this paper, we propose a method for sea ice image classification based on deep learning and heterogeneous data fusion. Utilizing the advantages of convolutional neural networks (CNNs) in terms of depth feature extraction, we designed a deep learning network structure for SAR and optical images and achieve sea ice image classification through feature extraction and a feature level fusion of heterogeneous data. For the SAR images, the improved spatial pyramid pooling (SPP) network was used and texture information on sea ice at different scales was extracted by depth. For the optical data, multi level feature information on sea ice such as spatial and spectral information on different types of sea ice was extracted through a path aggregation network (PANet) which enabled low level features to be fully utilized due to the gradual feature extraction of the convolution neural network. In order to verify the effectiveness of the method, two sets of heterogeneous sentinel satellite data were used for sea ice classification in the Hudson Bay area. The experimental results show that compared with the typical image classification methods and other heterogeneous data fusion methods, the method proposed in this paper fully integrates multi scale and multi level texture and spectral information from heterogeneous data and achieves a better classification effect (96.61% 95.69%