Abstract
We explore new and existing convolutional neural network (CNN) architectures for sea ice classification using Sentinel-1 (S1) synthetic aperture radar (SAR) data by investigating two key challenges: binary sea ice versus open-water classification, and a multi-class sea ice type classification. The analysis of sea ice in SAR images is challenging because of the thermal noise effects and ambiguities in the radar backscatter for certain conditions that include the reflection of complex information from sea ice surfaces. We use manually annotated SAR images containing various sea ice types to construct a dataset for our Deep Learning (DL) analysis. To avoid contamination between classes we use a combination of near-simultaneous SAR images from S1 and fine resolution cloud-free optical data from Sentinel-2 (S2). For the classification, we use data augmentation to adjust for the imbalance of sea ice type classes in the training data. The SAR images are divided into small patches which are processed one at a time. We demonstrate that the combination of data augmentation and training of a proposed modified Visual Geometric Group 16-layer (VGG-16) network, trained from scratch, significantly improves the classification performance, compared to the original VGG-16 model and an ad hoc CNN model. The experimental results show both qualitatively and quantitatively that our models produce accurate classification results.
Highlights
Sea ice is a key environmental factor [1] that significantly affects polar ecosystems.Over the past decade, the Arctic has experienced dramatic climate change that affects its environment, ecology, and meteorology
The patch-wise results of the ad hoc convolutional neural network (CNN) are presented in the second row, the results of the Visual Geometric Group 16-layer (VGG-16) model trained with transfer learning are presented in the third row, the results of the VGG-16 model retrained from scratch without the augmented data are presented in the fourth row, the results of the VGG-16 model retrained from scratch with the augmented data are presented in the fifth row, and the results of the modified VGG-16 model trained from scratch with augmented data are presented in the sixth row
We explored the potential of different CNN models for sea ice classification
Summary
Sea ice is a key environmental factor [1] that significantly affects polar ecosystems.Over the past decade, the Arctic has experienced dramatic climate change that affects its environment, ecology, and meteorology. The trends are more pronounced than in other regions, and this has been called the Arctic amplification [2] resulting in increasingly variable Arctic weather and sea ice conditions. These are already more extreme than at lower latitudes, and present challenges and threats to maritime operations related to resource exploitation, fisheries, and tourism in the northern areas [3,4]. Han et al [8] introduced a method for sea ice image classification based on feature extraction and a feature-level fusion of heterogeneous data from SAR and optical images. Song et al [9] proposed a method based on the combination of spatial and temporal features, derived from residual convolutional neural networks (ResNet) and long
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