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

Zhang, Chengqian; Chen, Xiao; Ji, Shunying

doi:10.1016/j.jag.2022.102885

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…In addition, a fast online shipborne system was developed and validated in [14] for ice detection and estimation of their locations to provide "ground truth" information supporting satellite observations. Ice-Deeplab [134] was developed to segment airborne images into three classes: Ocean, Ice and Sky. Zhao et al [135] improved the U-Net network by introducing Vgg-16 and ResNet-50 for encoding, constructing the new networks VU-Net and RU-Net, and achieved good results in testing with mid-high-latitude winter sea ice images captured by airborne cameras.…”

Section: Supervised Learningmentioning

confidence: 99%

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Huang,

Yu,

et al. 2024

Remote Sensing

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Deep learning, which is a dominating technique in artificial intelligence, has completely changed image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications and future trends. Our review focuses on research published from 2016 to the present, with a specific focus on deep-learning-based approaches in the last five years. We divided all related algorithms into three categories, including the conventional image classification approach, the machine learning-based approach and deep-learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in four aspects including climate research, navigation, geographic information systems (GIS) production and others. This paper also provides insightful observations and inspiring future research directions.

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Section: Supervised Learningmentioning

confidence: 99%

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Huang,

Yu,

et al. 2024

Remote Sensing

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“…Considering unbalanced frequency distribution of training dataset mentioned in Section II, the weighted L2 loss function is used to increase the modeling capability of the network for the SIC values with a small number of pixels. The weight is defined as (1), which is negatively correlated with the frequency of the ith SIC category…”

Section: Weighted Loss Functionmentioning

confidence: 99%

“…undergoing major rapid changes, such as smaller in area and thinner in thickness [1], [2], which is altering global weather conditions and climate. As one of the important physical parameters of sea ice, sea ice concentration (SIC) describes the percentage of sea ice per unit ocean area, intuitively reflecting the amount of sea ice, and can be used for the estimation of other important sea ice parameters (e.g., sea ice area) as well as some representative sea ice features (e.g., polynyas and leads) [3].…”

mentioning

confidence: 99%

Super-Resolution-Aided Sea Ice Concentration Estimation From AMSR2 Images by Encoder–Decoder Networks With Atrous Convolution

Feng

Liu

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Passive microwave data is an important data source for the continuous monitoring of Arctic-wide sea ice concentration (SIC). However, its coarse spatial resolution leads to blurring effects at the ice-water divides, resulting in the great challenges of fine-scale and accurate SIC estimation, especially for regions with low SIC. Besides, the SIC derived by operational algorithms using high-frequency passive microwave observations has great uncertainties in open water or marginal ice zones due to atmospheric effects. In this article, a novel framework is proposed to achieve accurately SIC estimation with improved spatial details from original low-resolution Advanced Microwave Scanning Radiometer 2 (AMSR2) images, with joint the super-resolution (SR) and SIC estimation network. Based on the SR network, the spatial resolution of original AMSR2 images can be improved by four times, benefiting to construct AMSR2 SR features with more high-frequency information for SIC estimation. The SIC network with an encoder-decoder structure and atrous convolution, is employed to accurately perform the SIC retrieval by considering the characteristics of passive microwave images in the Arctic sea ice region. Experimental results show that the proposed SR-Aided SIC estimation approach can generate accurate SIC with more detailed sea ice textures and much sharper sea ice edges. With respect to MODIS SIC products distributed in Arctic scale, the proposed model achieves a root-mean-square error (RMSE) of 5.94% and mean absolute error (MAE) of 3.04%, whereas the Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) SIC results have three and two times greater values of RMSE and MAE.

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“…They found a region of misclassification over open water associated with subswath banding effect when evaluating their model on a full Sentinel-1 scenes. Zhang et al [25] used a model architecture that is also based on the ASPP module. However, instead of satellite data, they were interested in performing semantic segmentation using data from video cameras of a Chinese ice-strengthened cargo ship.…”

Section: Related Workmentioning

confidence: 99%

Model Ensemble With Dropout for Uncertainty Estimation in Sea Ice Segmentation Using Sentinel-1 SAR

Pires de Lima,

Karimzadeh

2023

IEEE Trans. Geosci. Remote Sensing

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Despite the growing use of deep learning in sea ice mapping with SAR imagery, the study of model uncertainty and segmentation results remains limited. Deep learning models often produce overconfident predictions, a concern in sea ice mapping where misclassification can impact marine navigation safety. We incorporate and compare dropout and model ensemble within a convolutional neural network segmentation architecture to highlight regions with prediction uncertainty, and explore the impact of loss function choice. We evaluate model generalization and uncertainty characterization by training and evaluating models on the AI4Arctic Sea Ice Challenge Dataset (primary). We further explore model uncertainty by testing the trained models on the Extreme Earth version 2 Dataset (secondary). The primary and secondary datasets vary in number of scenes as well as in the available data and preprocessing. We obtain test F1 results higher than 0.97 for the primary dataset. Although the F1 performance for the secondary dataset is reduced to 0.93, the generated sea ice maps are reasonable across several Sentinel-1 scenes, and our proposed strategy helps in identification of misclassified and uncertain regions for human quality control. Our models seem to be robust against banding noise in Sentinel-1 SAR, and the prediction uncertainty frequently highlights ice regions misclassified as water, indicating its potential for realworld applications. Our study advances the field of machine learning-based sea ice mapping and highlights the importance of uncertainty estimation and cross-dataset evaluation for model development and deployment. Our approach can be adopted for other remote sensing applications as well.

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

Cited by 11 publications

References 31 publications

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Sea Ice Extraction via Remote Sensing Imagery: Algorithms, Datasets, Applications and Challenges

Super-Resolution-Aided Sea Ice Concentration Estimation From AMSR2 Images by Encoder–Decoder Networks With Atrous Convolution

Model Ensemble With Dropout for Uncertainty Estimation in Sea Ice Segmentation Using Sentinel-1 SAR

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