Deep Learning Based Sea Ice Classification with Gaofen-3 Fully Polarimetric SAR Data

Tianyu Zhang,Fengming Hui,Ying Yang,Mohammed Shokr,Xiao Cheng,Chunlei Mi,Xiao-Ming Li

doi:10.3390/rs13081452

Abstract

In this paper, the performance of C-band synthetic aperture radar (SAR) Gaofen-3 (GF-3) quad-polarization Stripmap (QPS) data is assessed for classifying late spring and summer sea ice types. The investigation is based on 18 scenes of GF-3 QPS data acquired in the Arctic Ocean in 2017. In this study, floe ice (FI), brash ice (BI) between floes and open water (OW, ice-free area) were classified based on a mini sea ice residual convolutional network, which we call MSI-ResNet. While investigating the optimal patch size for MSI-ResNet, we found that, as the patch size continues to grow, the classification accuracy first increases and then decreases. A patch size of 31 × 31 was found to achieve the best performance. The performance of classification using different polarization combinations from the QPS data was also assessed. The vertical-vertical (VV) polarization input overestimates the FI category while incorrectly identifying most of the BI as FI. The VH polarization produces a synchronous improvement in FI, BI, and OW discrimination, with a higher overall accuracy and kappa coefficient (91.09% and 0.85, respectively) than the VV polarization (83.37% and 0.70, respectively). The combination of VV and vertical-horizontal (VH) polarizations presents a modest precision improvement for BI and OW together with a slight overestimation for FI. With VV, VH, and horizontal-horizontal (HH) polarization data as the inputs, the user’s accuracy improves to 95.12%, 93.42%, and 95.17% for FI, BI, and OW, respectively. The accuracy was assessed against visual interpretation of the sea ice classes in the images using a stratified sampling method. The application of the MSI-ResNet method to data covering the Beaufort Sea and the north of the Severnaya Zemlya archipelago was found to achieve a high overall accuracy (kappa) of 94.62% (±0.92) and 94.23% (±0.90), respectively. This is similar to the classification accuracy obtained in the Fram Strait. From the results of this study, it is shown that the MSI-ResNet method performs better than the classical support vector machine (SVM) classifier for sea ice discrimination. The GF-3 QPS mode data also show more details in discriminating scattered sea ice floes than the coincident Sentinel-1A Extra Wide (EW) swath mode data.

Highlights

IntroductionInformation about sea ice type is important for ship navigation and climate change prediction in polar regions [1,2,3,4,5]
Polar sea ice is a sensitive indicator of global climate changes
It causes the melting of thin first-year ice (FYI) and the expansion of the open water area, which increases the mobility of the ice floes and leads to formation of more brash ice

Summary

Introduction

Information about sea ice type is important for ship navigation and climate change prediction in polar regions [1,2,3,4,5]. As the imaging mechanism is triggered by surface roughness and subsurface physical properties, SAR can be used to distinguish the different types of sea ice. A few milestones among the SAR systems that have been used to monitor and research Arctic sea ice are NASA’s SeaSAT mission, the series of satellites operated by the European Space Agency (ESA) (the European Remote Sensing, ENVISAT, and Sentinel-1 systems), the Japan Aerospace Exploration Agency (JAXA) Advanced Land Observing

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