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.

中文翻译：

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基于多级特征融合和残差网络的遥感海冰图像分类

海冰灾害已成为我国渤海地区最严重的海洋灾害之一，严重影响了沿海经济发展和居民生活。海冰分类是海冰检测的重要组成部分。高光谱影像和多光谱影像包含丰富的光谱信息和空间信息，为海冰分类提供重要数据支持。目前，大多数海冰分类方法主要侧重于基于光谱特征的浅层学习，深度学习方法在遥感影像分类中的良好性能为海冰分类提供了新思路。但是由于海冰图像分类中输入大小的影响，深度学习的水平有限，无法充分挖掘图像中的深层特征，这影响了海冰分类精度的进一步提高。因此，本文提出了一种基于残差网络的多级特征融合的图像分类方法。首先利用PCA方法提取原始图像的第一主成分，利用残差网络加深网络层数。FPN、PAN、SPP模块增加了层与层特征之间的挖掘，合并不同层之间的特征，进一步提高了海冰分类的准确性。为了验证本文方法的有效性，分别对2008年渤海湾高光谱影像和2020年渤海湾多光谱影像进行了海冰分类实验。