Sea Ice Images Research Articles

Using the Neural Network Technique for Lead Detection in Radar Images of Arctic Sea Ice

Using the Neural Network Technique for Lead Detection in Radar Images of Arctic Sea Ice

Observational Evidence of Cold Filamentary Intensification in an Energetic Meander of the Antarctic Circumpolar Current

Abstract Eddy stirring at mesoscale oceanic fronts generates finescale filaments, visible in submesoscale-resolving model simulations and high-resolution satellite images of sea surface temperature, ocean color, and sea ice. Submesoscale filaments have widths of O(1–10) km and evolve on time scales of hours to days, making them extremely challenging to observe. Despite their relatively small scale, submesoscale processes play a key role in the climate system by providing a route to dissipation; altering the stratification of the ocean interior; and generating strong vertical velocities that exchange heat, carbon, nutrients, and oxygen between the mixed layer and the ocean interior. We present a unique set of in situ and satellite observations in a standing meander region of the Antarctic Circumpolar Current (ACC) that supports the theory of cold filamentary intensification—revealing enhanced vertical velocities and evidence of subduction and ventilation associated with finescale cold filaments. We show that these processes are not confined to the mixed layer; EM-APEX floats reveal enhanced downward velocities (>100 m day−1) and evidence of ageostrophic motion extending as deep as 1600 dbar, associated with a ∼20-km-wide cold filament. A finer-scale (∼5 km wide) cold filament crossed by a towed Triaxus is associated with anomalous chlorophyll and oxygen values extending at least 100–200 dbar below the base of the mixed layer, implying recent subduction and ventilation. Energetic standing meanders within the weakly stratified ACC provide an environment conductive to the generation of finescale filaments that can transport water mass properties across mesoscale fronts and deep into the ocean interior.

Applications of Deep Learning-Based Super-Resolution Networks for AMSR2 Arctic Sea Ice Images

Studies have indicated that the decrease in the extent of Arctic sea ice in recent years has had a significant impact on the Arctic ecosystem and global climate. In order to understand the evolution of sea ice, it is becoming increasingly imperative to have continuous observations of Arctic-wide sea ice with high spatial resolution. Passive microwave sensors have the benefit of being less susceptible to weather, wider coverage, and higher temporal resolution. However, it is challenging to retrieve accurate parameters of sea ice due to the low spatial resolution of passive microwave images. Therefore, improving the spatial resolution of passive microwave images is beneficial for reducing the uncertainty of sea ice parameters. In this paper, four competitive multi-image super-resolution (MISR) networks are selected to explore the applicability of the networks on multi-frequency Advanced Microwave Scanning Radiometer 2 (AMSR2) images of Arctic sea ice. The upsampling factor is set to 4 in the experiment. Firstly, the optimal input lengths of the image sequence for the four MISR networks are found, and then the best network on different frequency band images is further identified. Furthermore, some factors, including seasons, sea ice motion, and polarization mode of images, that may affect the super-resolution (SR) results are analyzed. The experimental results indicate that utilizing images from winter yields superior SR results. Conversely, SR results are the worst during summer across all four MISR networks, exhibiting the largest difference in PSNR of 4.48 dB. Additionally, the SR performance is observed to be better for images with smaller magnitudes of sea ice motion compared to those with larger motions, with the maximum PSNR difference of 2.04 dB. Finally, the SR results for vertically polarized images surpass those for horizontally polarized images, showcasing an average advantage of 4.02 dB in PSNR and 0.0061 in SSIM. In summary, valuable suggestions for selecting MISR models for passive microwave images of Arctic sea ice at different frequency bands are offered in this paper. Additionally, the quantification of the various impact factors on SR performance is also discussed in this paper, which provides insights into optimizing MISR algorithms for passive microwave sea ice imagery.

Improved Sea Ice Image Segmentation Using U2-Net and Dataset Augmentation

Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and severe weather conditions affect image quality, which affects the accuracy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U2-Net network was constructed using multiscale inflation convolution and a multilayer convolutional block attention module (CBAM) attention mechanism for the U2-Net network. The experiments showed that (1) data augmentation solved the problem of an insufficient number of training samples to a certain extent and improved the accuracy of image segmentation; (2) this study designed a multilevel Gaussian noise data augmentation scheme to improve the network’s ability to resist noise interference and achieve a more accurate segmentation of images with different degrees of noise pollution; (3) the inclusion of a multiscale inflation perceptron and multilayer CBAM attention mechanism improved the ability of U2-Net network feature extraction and enhanced the model accuracy and generalization ability.

Inhomogeneous regularization with limited and indirect data

For an ill-posed inverse problem, particularly with incomplete and limited measurement data, regularization is an essential tool for stabilizing the inverse problem. Among various forms of regularization, the ℓp penalty term provides a suite of regularization of various characteristics depending on the value of p. When there are no explicit features to determine p, a spatially varying inhomogeneous p can be incorporated to apply different regularization characteristics that change over the domain. This study proposes a strategy to design the exponent p when the first and second derivatives of the true signal are not available, such as in the case of indirect and limited measurement data. The proposed method extracts statistical and patch-wise information using multiple reconstructions from a single measurement, which assists in classifying each patch to predefined features with corresponding p values. We validate the robustness and effectiveness of the proposed approach through a suite of numerical tests in 1D and 2D, including a sea ice image recovery from partial Fourier measurement data. Numerical tests show that the exponent distribution is insensitive to the choice of multiple reconstructions.

Sea Ice Classification Using Mutually Guided Contexts

In this paper, sea ice classification on a remote sensing image given just a small number of labeled pixels is investigated. Effective sea ice classification is rendered from two aspects. First, a feature extraction method is developed. It extracts the context feature from a classification map. Second, an iterative learning paradigm is established. The labeled pixels are divided into two training subsets. At each iteration, the context feature for one subset is extracted from the classification map which is obtained subject to the other subset. Therefore, the two subsets mutually guide each other for updating the context feature in an iterative manner, which finally renders effective sea ice classification. The above paradigm is referred to as mutually guided contexts. The advantages of the new paradigm are two-fold. First, the context feature enriches the sea ice image representation in a general manner regardless of the types of raw image data. Second, the two training subsets keep providing different refined classification maps for each other such that the comprehensiveness of the context feature is recursively enhanced. Therefore, the paradigm of mutually guided contexts comprehensively characterizes the sea ice image representation for training and classification even when only small training data are available. Experiments validate the effectiveness of the mutually guided contexts for sea ice classification.

Horizon detection and tracking in sea-ice conditions using machine vision

An automated process is proposed for horizon detection and tracking using machine vision cameras and in polar, sea-ice conditions. These conditions present unique challenges for machine vision applications, such as a large amount of clutter (e.g. icebergs) and secondary edge lines from broken ice pieces. The process is divided in two parts: a more computationally expensive, yet robust detection algorithm in the first stage, based on Convolutional Neural Networks, and used to detect the horizon line in an arbitrary sea-ice image; followed by a tracking algorithm, responsible of efficiently detecting the horizon line in the subsequent images of a sequence. We propose two tracking algorithms, one based on the traditional Canny and Hough line detection methods; and a second novel approach using entropy as a measure of randomness, to segment between sea-ice and sky. Our automated process was compared to manually obtained ground-truth data and the results indicate good agreement, especially for the texture-based tracking algorithm.

Sea ice image classification based on ResFPG network and heterogeneous data fusion

ABSTRACT Sea ice detection has played an important role in climate protection and strategic deployment. Due to the autonomous learning characteristics of deep learning, it has been gradually applied to the classification of remote sensing sea ice images. At present, deep learning models are mostly used in sea ice classification of single source remote sensing data. Due to the limitations of single source data and the information loss of deep learning model in the process of feature extraction layer by layer, it is inevitable to encounter a bottleneck in sea ice detection requiring fine classification. To solve the above problems, this paper proposes a sea ice image classification method based on ResNet16-feature pyramid networks-spatial pyramid pooling-gated fusion network (ResFPG) and heterogeneous data fusion. In the feature extraction part, the method uses the improved ResNet16 to extract the multi-level feature information of sea ice in synthetic aperture radar data and optical data, reducing information loss in the feature extraction process, then mines and fuses the low-level spatial information and high-level semantic information through the improved feature pyramid networks (FPN), and then collects and fuses the output features of different scales through the spatial pyramid pooling (SPP) network. In the feature fusion part, a gated feature-level fusion strategy is designed to further improve the overall classification accuracy by adaptively adjusting the feature contribution of two heterogeneous sources of data through the gated fusion network (GFN). In order to verify the effectiveness of this method, we use two sets of heterogeneous sea ice remote sensing data located in Hudson Bay area for experiments. The experimental results show that compared with other image classification methods, the proposed method fully excavates and integrates the multi-scale and multi-level features in heterogeneous data, effectively distinguishes the feature contribution, and achieves better classification results (97.14% and 95.85%).

Semantic image segmentation for sea ice parameters recognition using deep convolutional neural networks

• A novel CNN-based model was developed for the recognition of sea-ice images. • A modified decoder by combining shallow and deep layer features. • The designed network achieves good results for the segmentation of various scale objects. • A low-cost, and automatic method for the computation of sea-ice parameters. An accurate algorithm for sea ice segmentation is critical for monitoring sea ice parameters of ship navigation in ice-covered seas, as it can automatically extract ice objects and corresponding information to compute essential parameters such as surface ice concentration and ice floe size. In this paper, based on digital images captured by onboard cameras, a novel network called Ice-Deeplab for pixel-wise ice image segmentation is proposed. The Ice-Deeplab network is constructed using the deep convolutional neural network Deeplab and is modified with an attention module and an improved decoding structure. To investigate its reliability, the Ice-Deeplab network is applied to a 320-image dataset, with 80% for training and 20% for validation. The experiments demonstrated that the proposed Ice-Deeplab yields better segmentation results than the original Deeplab model under different validation scenarios, achieving an overall accuracy of 90.5% among the classes sea-ice, ocean, and sky. Moreover, the proposed model was applied to un-labelled test data to demonstrate its generalisation ability for real-time ice segmentation.

An Improved Sea Ice Classification Algorithm with Gaofen-3 Dual-Polarization SAR Data Based on Deep Convolutional Neural Networks

The distribution of sea ice is one of the major safety hazards for sea navigation. As human activities in polar regions become more frequent, monitoring and forecasting of sea ice are of great significance. In this paper, we use SAR data from the C-band synthetic aperture radar (SAR) Gaofen-3 satellite in the dual-polarization (VV, VH) fine strip II (FSII) mode of operation to study the Arctic sea ice classification in winter. SAR data we use were taken in the western Arctic Ocean from January to February 2020. We classify the sea ice into four categories, namely new ice (NI), thin first-year ice (tI), thick first-year ice (TI), and old ice (OI), by referring to the ice maps provided by the Canadian Ice Service (CIS). Then, we use the deep learning model MobileNetV3 as the backbone network, input samples of different sizes, and combine the backbone network with multiscale feature fusion methods to build a deep learning model called Multiscale MobileNet (MSMN). Dual-polarization SAR data are used to synthesize pseudocolor images and produce samples of sizes 16 × 16 × 3, 32 × 32 × 3, and 64 × 64 × 3 as input. Ultimately, MSMN can reach over 95% classification accuracy on testing SAR sea ice images. The classification results using only VV polarization or VH polarization data are tested, and it is found that using dual-polarization data could improve the classification accuracy by 10.05% and 9.35%, respectively. When other classification models are trained using the training data from this paper for comparison, the accuracy of MSMN is 4.86% and 1.84% higher on average than that of the model built using convolutional neural networks (CNNs) and ResNet18 model, respectively.

A ship navigation information service system for the Arctic Northeast Passage using 3D GIS based on big Earth data

ABSTRACT Research on Arctic passages has mainly focused on navigation policies, sea ice extraction models, and navigation of Arctic sea routes. It is difficult to quantitatively address the specific problems encountered by ships sailing in the Arctic in real time through traditional manual approaches. Additionally, existing sea ice information service systems focus on data sharing and lack online calculation and analysis capabilities, making it difficult for decision-makers to derive valuable information from massive amounts of data. To improve navigation analysis through intelligent information service, we built an advanced Ship Navigation Information Service System (SNISS) using a 3D geographic information system (GIS) based on big Earth data. The SNISS includes two main features: (1) heuristic algorithms were developed to identify the optimal navigation route of the Arctic Northeast Passage (NEP) from a macroscale perspective for the past 10 years to the next 100 years, and (2) for key sea straits along the NEP, online local sea-ice images can be retrieved to provide a fully automatic sea ice data processing workflow, solving the problems of poor flexibility and low availability of real sea ice remote sensing data extraction. This work can potentially enhance the safety of shipping navigation along the NEP.

Improved Sea-Ice Identification Using Semantic Segmentation With Raindrop Removal

Sea-ice identification is an essential process for safety critical navigation support of surface vessels in polar waters. Semantic segmentation has drawn much attention as an enabling technique for fast detection of objects in a scene including sea-ice conditions. Identifying sea-ice is a challenging problem, especially in the presence of raindrops. The raindrop alters the boundaries of the objects in the scene, and thus, degrades the identification performance. In this work, a raindrop removing framework is developed to enhance the classification performance. Three deep-learning semantic segmentation networks are trained to classify the scene of sea-ice images into ice, water, ship, and sky. The deep-learning networks are VGG-16, fully convolutional network, and pyramid scene parsing network. Transfer learning along with data augmentation operations have been implemented to improve the training process. Results illustrate that data augmentation operations enhance the performance of the three models. Moreover, the raindrop removing framework improves the models’ performance, e.g. the average intersection over union of the VGG-16 model is improved from 85.91% to 91.70%.

Partial Shape Recognition for Sea Ice Motion Retrieval in the Marginal Ice Zone from Sentinel-1 and Sentinel-2

We present an algorithm for computing ice drift in the marginal ice zone (MIZ), based on partial shape recognition. With the high spatial resolution of Sentinel-1 and Sentinel-2 images, and the low sensitivity to atmospheric influences of Sentinel-1, a considerable quantity of ice floes is identified using a mathematical morphology method. Hausdorff distance is used to measure the similarity of segmented ice floes. It is tolerant to perturbations and deficiencies of floe shapes, which enhances the density of retrieved sea ice motion vectors. The PHD algorithm can be applied to sequential images from different sensors, and was tested on two combined image mosaics consisting of Sentinel-1 and Sentinel-2 data acquired over the Fram Strait; the PHD algorithm successfully produced pairs of matched ice floes. The matching result has been verified using shape and surface texture similarity of the ice floes. Moreover, the present method can naturally be extended to the problem of multi-source sea ice image registration.

Remote Sensing Sea Ice Image Classification Based on Multilevel Feature Fusion and Residual Network

Sea ice disasters are already one of the most serious marine disasters in the Bohai Sea region of our country, which have seriously affected the coastal economic development and residents’ lives. Sea ice classification is an important part of sea ice detection. Hyperspectral imagery and multispectral imagery contain rich spectral information and spatial information and provide important data support for sea ice classification. At present, most sea ice classification methods mainly focus on shallow learning based on spectral features, and the good performance of the deep learning method in remote sensing image classification provides a new idea for sea ice classification. However, the level of deep learning is limited due to the influence of input size in sea ice image classification, and the deep features in the image cannot be fully mined, which affects the further improvement of sea ice classification accuracy. Therefore, this paper proposes an image classification method based on multilevel feature fusion using residual network. First, the PCA method is used to extract the first principal component of the original image, and the residual network is used to deepen the number of network layers. The FPN, PAN, and SPP modules increase the mining between layer and layer features and merge the features between different layers to further improve the accuracy of sea ice classification. In order to verify the effectiveness of the method in this paper, sea ice classification experiments were performed on the hyperspectral image of Bohai Bay in 2008 and the multispectral image of Bohai Bay in 2020. The experimental results show that compared with the algorithm with fewer layers of deep learning network, the method proposed in this paper utilizes the idea of residual network to deepen the number of network layers and carries out multilevel feature fusion through FPN, PAN, and SPP modules, which effectively solves the problem of insufficient deep feature extraction and obtains better classification performance.

Deriving Sea Ice Images from Super Resolution SCATSAT-1 Data over the Antarctic: Operational Method and Accuracy Assessment

Deriving Sea Ice Images from Super Resolution SCATSAT-1 Data over the Antarctic: Operational Method and Accuracy Assessment

COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE

Abstract. The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation. Compared to optical imagery and SAR imagery, passive microwave images can be obtained for all-sky conditions with high time resolution. However, the spatial resolution of passive microwave images is relatively low (6.25 km – 25 km) for the observation of detailed sea ice characteristics and small-scale sea ice geographical phenomena. Therefore, in this paper, considering the suitability of different alignment and fusion strategies to the characteristics of passive microwave images of sea ice, two multi-images deep learning super-resolution (SR) algorithms, Recurrent Back-Projection Network (RBPN) and network of Temporal Group Attention (TGA), are selected to test the effects of SR technique for passive microwave images of sea ice. Both qualitative and quantitative comparisons are provided for the SR results oriented from two algorithms. Overall, the SR performance of TGA algorithm outperforms RBPN algorithm for the passive microwave images of sea ice.

Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with the PCA Network

Sea ice is one of the most prominent causes of marine disasters occurring at high latitudes. The detection of sea ice is particularly important, and the classification of sea ice images is an important part of sea ice detection. Traditional sea ice classification based on optical remote sensing mostly uses spectral information only and does not fully extract rich spectral and spatial information from sea ice images. At the same time, it is difficult to obtain samples and the resulting small sample sizes used in sea ice classification has limited the improvement of classification accuracy to a certain extent. In response to the above problems, this paper proposes a hyperspectral sea ice image classification method involving spectral-spatial-joint features based on the principal component analysis (PCA) network. First, the method uses the gray-level co-occurrence matrix (GLCM) and Gabor filter to extract textural and spatial information about sea ice. Then, the optimal band combination is extracted with a band selection algorithm based on a hybrid strategy, and the information hidden in the sea ice image is deeply extracted through a fusion of spectral and spatial features. Then, the PCA network is designed based on principal component analysis filters in order to extract the depth features of sea ice more effectively, and hash binarization maps and block histograms are used to enhance the separation and reduce the dimensions of features. Finally, the low-level features in the data form more abstract and invariant high-level features for sea ice classification. In order to verify the effectiveness of the proposed method, we conducted experiments on two different data collection points in Bohai Bay and Baffin Bay. The experimental results show that, compared with other single feature and spectral-spatial-joint feature algorithms, the proposed method achieves better sea ice classification results (94.15% and 96.86%) by using fewer training samples and a shorter training time.

Sea Ice Image Classification Based on Heterogeneous Data Fusion and Deep Learning

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%).

Automatic Sea-Ice Classification of SAR Images Based on Spatial and Temporal Features Learning

Sea ice has a significant effect on climate change and ship navigation. Hence, it is crucial to draw sea-ice maps that reflect the geographical distribution of different types of sea ice. Many automatic sea-ice classification methods using synthetic aperture radar (SAR) images are based on the polarimetric characteristics or image texture features of sea ice. They either require professional knowledge to design the parameters and features or are sensitive to noise and condition changes. Moreover, ice changes over time are often ignored. In this article, we propose a new SAR sea-ice image classification method based on a combined learning of spatial and temporal features, derived from residual convolutional neural networks (ResNet) and long short-term memory (LSTM) networks. In this way, we achieve automatic and refined classification of sea-ice types. First, we construct a seven-type ice data set according to the Canadian Ice Service ice charts. We extract spatial feature vectors of a time series of sea-ice samples using a trained ResNet network. Then, using the feature vectors as inputs, the LSTM network further learns the variation of the set of sea-ice samples with time. Finally, the extracted high-level features are fed into a softmax classifier to output the most recent ice type. Taking both spatial features and time variation into consideration, our method can achieve a high classification accuracy of 95.7% for seven ice types. Our method can automatically produce more objective sea-ice interpretation maps, allowing detailed sea-ice distribution and improving the efficiency of sea-ice monitoring tasks.

Shifting surface

This research explores how satellite images of Arctic sea ice contribute to climate change discourse. Different discourses require distinct responses. Policy measures are contingent upon representation, be it i.e. a threat or opportunity. The representations discussed are by the NSIDC and NASA, which hold a visual hegemony. First, the introduction discusses visual studies in policy research and identifies a simplified dichotomy of a threat discourse and environmental citizenship. Moreover, the methodology of visual discourse analysis based on poststructuralism is described. The delineated images portray a vertical, planar view allowing for spatial reference. Arctic sea ice is a visible climate change effect and the absence of boundaries, intervisuality with the Earthrise icon and focus on environmental effects support a discourse of citizenship.