Combined Use of Space Borne Optical and SAR Data to Improve Knowledge about Sea Ice for Shipping

König, Christine; König, Thomas; Singha, Suman; Frost, Anja; Jacobsen, Sven

doi:10.3390/rs13234842

Cited by 3 publications

(1 citation statement)

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“…It is found that the identification of certain features such as crevasses, blue ice, melt ponds and lake ice has been improved with fused images compared to the original MS and SAR images. König et al 19 . presented the results of a first step toward a new combined product for sea ice classification generated from Sentinel-3 SLSTR and Sentinel-1 SAR data suitable for providing shipping support in Arctic and subarctic ocean waters.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing sea ice classification based on DenseNet and heterogeneous data fusion

Han

Shen

Hong

et al. 2022

J. Appl. Rem. Sens.

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.Most remote sensing sea ice classification methods use single-source remote sensing data, such as synthetic aperture radar (SAR) data and optical remote sensing data. SAR data contain rich sea ice texture information, but the data are relatively single, making it difficult to distinguish detailed sea ice categories. Optical data include abundant spatial-spectral information, but they are often affected by clouds, fog, and severe weather. Hence, given the limitations of single-source data, the remote sensing sea ice classification accuracy cannot be further improved. A sea ice classification method based on deep learning and multisource remote sensing data fusion is proposed utilizing an improved densely connected convolutional neural network (DenseNet) to mine and fuse the multilevel features of sea ice. According to the characteristics of SAR data and optical data, a dual-branch network structure based on an improved DenseNet is employed for feature extraction, and a squeeze-and-excitation attention mechanism is introduced to weight the fusion features to further enhance the feature weights that can effectively distinguish different types of sea ice. The fully connected network is used to perform the deep fusion of features and classify sea ice. To verify the effectiveness of the proposed method, two sets of sea ice data are utilized for sea ice classification. The experimental results show that the proposed method fully excavates and fuses the multilevel characteristics of heterogeneous data using the improved dual-branch network structure, leverages the complementary characteristics of SAR data and optical data, significantly increases the sea ice classification accuracy, and effectively improves the influence of cloud cover on the sea ice classification accuracy. Compared with the typical single-source data classification method and other heterogeneous data fusion classification methods, the proposed method achieves superior overall classification accuracy (98.49% and 98.58%).

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Section: Introductionmentioning

confidence: 99%