Objective

2

The classification of sea ice is an important task in sea ice monitoring. Synthetic aperture radar (SAR)

as an active microwave sensor

has all-weather

day-and-night

multi-view

and penetration imaging capabilities. SAR has been widely used for sea ice monitoring. Existing methods of automatic sea ice classification using SAR data are divided into two categories as follows:1) Classification based on the physical characteristics of sea ice and imaging characteristics of SAR imagery

such as the relationship between sea ice types and incidence angle

polarization mode

and backscatter coefficient of SAR image; SAR imagery requires professional background. 2) Traditional image classification methods with SAR imagery

such as support vector machine (SVM) and artificial neural network

must design features in advance and thus are limited by prior knowledge. In recent years

deep learning has achieved considerable success in image classification and object recognition. Image classification based on deep learning can automatically learn high-level features of sample data beyond low-level texture and color features

thus achieving a high accuracy of classification results without constraints of human prior knowledge. However

SAR images have different imaging mechanisms from optical imaging of ordinary camera

and sea ice shows lower identifiable characteristics on the SAR imagery than general ground objects with specific shape

texture

and other distinctive features. The effectiveness of deep learning models for sea ice classification remains unclear. We aim to use deep learning models

such as a convolutional neural network (CNN) and a deep belief network (DBN)

to classify sea ice and water in SAR imagery and evaluate the performance and influence factors of the two models to improve the accuracy and speed of sea ice classification.

Method

2

The entire process of sea ice-water classification experiment mainly includes four steps. First

the study area and the corresponding SAR images must be determined. Hudson Bay was selected

and 16 SAR images of the area were obtained from Sentinel-1 satellite. Second

an experimental data set must be constructed in accordance with the ice chart published by the Canadian Ice Service (CIS)

including image cutting

normalization

and labeling. A total of 2 000 training and 400 validation samples were prepared for each sample size

although the sample size varied from 16×16 pixels to 64×64 pixels. Eight regions were used for testing. Third

the structures of the CNN and DBN must be designed

and the influence of different network hyperparameters on classification performance must be discussed. Finally

the classification performance of models influenced by train patch size

data set size

layers of the models

sea ice proportion in the test image

and image filter size must be evaluated. The evaluation was based on the indices of precision ratio

recall ratio

F

1

score

and kappa coefficient.

Result

2

The CNN and DBN models reached more than 93% overall accuracy and 0.8 kappa coefficient given the pixel-level ground truth generated by SVM and manual correction. Regional concentration values computed by the classification results were close to the concentration data provided in the CIS ice chart with a mean squared error of 0.001 for CNN and 0.016 for DBN. The train patch size of sea ice significantly influenced the classification performance of the models

whereas the data set size and layers of the models only slightly influenced the classification performance of the models. Additional ice and water would be misclassified when the size of the training samples was large. Therefore

the sample size of the CNN and DBN should not be very large. Under our experimental conditions

the optimal train patch size of CNN and DBN was 16×16 pixels and 32×32 pixels

correspondingly. In addition

the sea ice-water ratio of the test samples affected the precision and recall ratios. The ice and water in terms of

F

1

score and the kappa coefficient stabilized when the sea ice-water ratio was at 0.5. The precision and recall of the DBN were relatively sensitive to the sea ice-water ratio of the test samples.

Conclusion

2

We evaluated the performance of the sea ice-water classification in SAR imagery with CNN and DBN. Notably

deep learning demonstrates considerable potential in the sea ice classification. The sea ice classification of SAR images based on deep learning without designing features in advance can be robustly applied to different SAR data products over several traditional classification methods

such as SVM. The classification method based on deep learning models can provide more convenient and more detailed sea ice interpretations than the complex production process of the CIS ice chart and the rough range of sea ice-type tagging information. Owing to the different resolutions of SAR images

the patch size of the optimal classification sample will be different. In this study

errors in the ground truth are inevitable considering the limitations of current sea ice observation methods; these limitations have been discussed. The main contributions of this study are as follows:1) In the sea ice-water classification using SAR images

two typical deep learning networks with different mechanisms have been used. The convolution operation of the CNN is suitable for exploring the local spatial correlation of images and has a robust application in the image recognition field. The restricted Boltzmann machine

as an important component of the DBN

is better at exploring probabilistic relationships among different elements and is more suitable for incremental learning than the CNN. The CNN showed better performance than the DBN in the classification of sea ice-water with SAR imagery. 2) A complete experimental procedure for sea ice classification of SAR images through deep learning methods

which can help guide related research

is summarized. 3) A new idea of using the deep learning method for sea ice classification is proposed. This method can shorten the existing process of creating a sea ice interpretation map and provide accurate geographic distribution information of a sea ice type. In our future work

we will compare the classification performance of different SAR data sources.