Abstract
Sea ice disasters are already one of the most serious marine disasters in the Bohai Sea region of our country, which have seriously affected the coastal economic development and residents’ lives. Sea ice classification is an important part of sea ice detection. Hyperspectral imagery and multispectral imagery contain rich spectral information and spatial information and provide important data support for sea ice classification. At present, most sea ice classification methods mainly focus on shallow learning based on spectral features, and the good performance of the deep learning method in remote sensing image classification provides a new idea for sea ice classification. However, the level of deep learning is limited due to the influence of input size in sea ice image classification, and the deep features in the image cannot be fully mined, which affects the further improvement of sea ice classification accuracy. Therefore, this paper proposes an image classification method based on multilevel feature fusion using residual network. First, the PCA method is used to extract the first principal component of the original image, and the residual network is used to deepen the number of network layers. The FPN, PAN, and SPP modules increase the mining between layer and layer features and merge the features between different layers to further improve the accuracy of sea ice classification. In order to verify the effectiveness of the method in this paper, sea ice classification experiments were performed on the hyperspectral image of Bohai Bay in 2008 and the multispectral image of Bohai Bay in 2020. The experimental results show that compared with the algorithm with fewer layers of deep learning network, the method proposed in this paper utilizes the idea of residual network to deepen the number of network layers and carries out multilevel feature fusion through FPN, PAN, and SPP modules, which effectively solves the problem of insufficient deep feature extraction and obtains better classification performance.
Highlights
Sea ice disasters are one of the marine disasters that should not be underestimated. ey mostly occur in polar regions and mid-to-high dimensional regions
Sea ice image classification is an important part of sea ice detection. e remote sensing images currently used for sea ice classification mainly include SAR images and optical images (multispectral images and hyperspectral images)
Literature [6] used the discriminant function to calculate the distance to the center, and the result shows that the minimum distance method can obtain higher classification accuracy when analyzing remote sensing images. e above supervised classification algorithms are all shallow models and cannot extract deep features in hyperspectral and multispectral images, which limits the further improvement of classification accuracy
Summary
Sea ice disasters are one of the marine disasters that should not be underestimated. ey mostly occur in polar regions and mid-to-high dimensional regions. China’s Bohai Bay is located in a mid-to-high dimensional area. Erefore, in order to avoid more economic losses and casualties, it is very necessary to detect sea ice in the Bohai Bay area [2]. E remote sensing images currently used for sea ice classification mainly include SAR images and optical images (multispectral images (such as Landsat, Sentinel, MODIS, and so on) and hyperspectral images). E optical image has a high spectral resolution, contains rich spectral and spatial information, can extract detailed features of different types of sea ice, and provides effective data support for accurate sea ice classification. Literature [4] improved the classification accuracy of sea ice images in Liaodong Bay by building decision trees and gray-level co-occurrence matrix (GLCM). Literature [6] used the discriminant function to calculate the distance to the center, and the result shows that the minimum distance method can obtain higher classification accuracy when analyzing remote sensing images. e above supervised classification algorithms are all shallow models and cannot extract deep features in hyperspectral and multispectral images, which limits the further improvement of classification accuracy
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