Multi-Featured Sea Ice Classification with SAR Image Based on Convolutional Neural Network
Multi-Featured Sea Ice Classification with SAR Image Based on Convolutional Neural Network
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DOI：
10.3390/rs15164014
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Hongyang Wan;Xiaowen Luo;Ziyin Wu;Xiaoming Qin;Xiaolun Chen;Bin Li;J. Shang;Dineng Zhao
中科院分区：
文献类型：
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作者：
Hongyang Wan;Xiaowen Luo;Ziyin Wu;Xiaoming Qin;Xiaolun Chen;Bin Li;J. Shang;Dineng Zhao
海冰是影响地球环境变化的重要因素。监测海冰非常重要，监测的主要目标之一就是对海冰进行分类。目前，合成孔径雷达(SAR)数据主要用于海冰分类，常用的选择是单一极化波段或简单的极化波段组合。虽然目前的大部分研究都集中在优化网络结构以实现高分类精度，这需要大量的训练资源，但我们的目标是从SAR数据中提取更多的信息用于分类。因此，我们提出了一种结合极化分解计算的极化特征和联合时频分析(JTFA)计算的谱图特征的多特征SAR海冰分类方法。为了学习海冰的多种特征，我们构建了一种卷积神经网络(CNN)结构，该结构结合了海冰的空间特征和物理属性，包括海冰的偏振特征和光谱特征。本文利用ALOS PALSAR SLC资料中的HH、HV、VH和VV四种极化类型进行多特征海冰分类。我们将海冰分为新冰(NI)、第一年冰(FI)、旧冰(OI)、变形冰(DI)和开阔水域(OW)。然后利用混淆矩阵进行精度计算，并进行对比分析。实验结果表明，本文提出的多特征方法能够在较小的数据量和计算量下达到较高的准确率。在选取的4个场景中，总体准确率分别达到了95%、91%、96%和95%，与单一特征的海冰分类方法相比有了显著的提高。
Sea ice is a significant factor in influencing environmental change on Earth. Monitoring sea ice is of major importance, and one of the main objectives of this monitoring is sea ice classification. Currently, synthetic aperture radar (SAR) data are primarily used for sea ice classification, with a single polarization band or simple combinations of polarization bands being common choices. While much of the current research has focused on optimizing network structures to achieve high classification accuracy, which requires substantial training resources, we aim to extract more information from the SAR data for classification. Therefore we propose a multi-featured SAR sea ice classification method that combines polarization features calculated by polarization decomposition and spectrogram features calculated by joint time-frequency analysis (JTFA). We built a convolutional neural network (CNN) structure for learning the multi-features of sea ice, which combines spatial features and physical properties, including polarization and spectrogram features of sea ice. In this paper, we utilized ALOS PALSAR SLC data with HH, HV, VH, and VV, four types of polarization for the multi-featured sea ice classification method. We divided the sea ice into new ice (NI), first-year ice (FI), old ice (OI), deformed ice (DI), and open water (OW). Then, the accuracy calculation by confusion matrix and comparative analysis were carried out. Our experimental results demonstrate that the multi-feature method proposed in this paper can achieve high accuracy with a smaller data volume and computational effort. In the four scenes selected for validation, the overall accuracy could reach 95%, 91%, 96%, and 95%, respectively, which represents a significant improvement compared to the single-feature sea ice classification method.