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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Sea Ice Classification Using TerraSAR-X ScanSAR Data With Removal of Scalloping and Interscan Banding
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Sea Ice Classification Using TerraSAR-X ScanSAR Data With Removal of Scalloping and Interscan Banding

机译：SEA ICE分类使用Terrasar-X ScanSAR数据进行拆除扇形和跨扫描系

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摘要

Effective methods of sea ice classification are crucial to regional ice type mapping using spaceborne synthetic aperture radar (SAR), especially in ScanSARmode. However, in TerraSAR-X ScanSAR images of ocean scenes, the scalloping and interscan banding (ISB) artifacts are usually visible. Though these two artifacts could be reduced to a certain degree where residual artifacts do not impede the visual interpretation of SAR images, this is not the case when it comes to sea ice classification. The difficulty mainly comes from the involvement of texture features in sea ice classification. Texture features, especially gray level co-occurrence matrix (GLCM) are useful bases for sea ice classification using SAR data in single or dual polarization. When GLCM is applied to TerraSAR-X ScanSAR data, however, scalloping and ISB artifacts are found to be intensified, further affecting the classification result. How to largely eliminate the effects of scalloping and ISB on sea ice classification has not been studied thoroughly yet. In this paper, an approach combining Kalman filter, GLCM, and support vector machine is proposed. An independent testing shows that this approach is effective at the removal of scalloping and ISB's effects on sea ice classification using TerraSAR-X ScanSAR data, with the overall accuracy of 88.26%. Besides, convolutional neural network is implemented to compare with the proposed approach.
机译：有效的海冰分类方法对于使用星载合成孔径雷达（SAR）的区域冰型映射至关重要，尤其是在SCANSMode中。然而，在海洋场景的Terrasar-X Scansar图像中，扇形和间隙条带（ISB）伪像通常可见。虽然这两个工件可以减少到一定程度，但是残留伪像不会阻碍SAR图像的视觉解释，但在海冰分类方面并非如此。困难主要来自海冰分类中的纹理特征的参与。纹理特征，尤其是灰度共同发生矩阵（GLCM）是使用单一或双极化的SAR数据的海冰分类的有用基础。然而，当GLCM应用于Terrasar-X ScanSAR数据时，发现扇形和ISB伪像被发现会加剧，进一步影响分类结果。如何在很大程度上消除扇形和ISB对海冰分类的影响尚未彻底研究。本文提出了一种结合卡尔曼滤波器，GLCM和支持向量机的方法。独立测试表明，这种方法在使用Terrasar-X Scansar数据中删除扇形和ISB对海冰分类的影响有效，整体准确性为88.26％。此外，实现了卷积神经网络以与所提出的方法进行比较。

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  • 作者

    Zhang Lu; Liu Huiying; Gu Xinwei; Guo Huadong; Chen Jie; Liu Guang;

  • 作者单位

    Chinese Acad Sci Inst Remote Sensing & Digital Earth CAS Key Lab Digital Earth Sci Beijing 100094 Peoples R China|Key Lab Earth Observat Hainan Prov Sanya 572029 Hainan Peoples R China;

    Chinese Acad Sci Inst Remote Sensing & Digital Earth CAS Key Lab Digital Earth Sci Beijing 100094 Peoples R China|Univ Chinese Acad Sci Beijing 100094 Peoples R China;

    Beihang Univ Sch Elect & Informat Engn Beijing 100083 Peoples R China;

    Chinese Acad Sci Inst Remote Sensing & Digital Earth CAS Key Lab Digital Earth Sci Beijing 100094 Peoples R China|Univ Chinese Acad Sci Beijing 100094 Peoples R China;

    Beihang Univ Sch Elect & Informat Engn Beijing 100083 Peoples R China;

    Chinese Acad Sci Inst Remote Sensing & Digital Earth CAS Key Lab Digital Earth Sci Beijing 100094 Peoples R China|Univ Chinese Acad Sci Beijing 100094 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Classification; kalman filter; sea ice; synthetic aperture radar (SAR);

    机译：分类;卡尔曼过滤器;海冰;合成孔径雷达（SAR）;

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