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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Penalized Linear Discriminant Analysis of Hyperspectral Imagery for Noise Removal
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Penalized Linear Discriminant Analysis of Hyperspectral Imagery for Noise Removal

机译：高光谱图像的降噪线性判别分析

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摘要

The existence of noise in hyperspectral ima-gery (HSI) seriously affects image quality. Noise removal is one of the most important and challenging tasks to complete before hyperspectral information extraction. Though many advances have been made in alleviating the effect of noise, problems, including a high correlation among bands and predefined structure of noise covariance, still prevent us from the effective implementation of hyperspectral denoising. In this letter, a new algorithm named the penalized linear discriminant analysis (PLDA) and noise adjusted principal components transformation (NAPCT) was proposed. PLDA was applied to search for the best noise covariance structure, while the NAPCT was employed to remove the noise. The results of the tests with both HJ-1A HSI and EO-1 Hyperion showed that the proposed PLDA-NAPCT method could remove the noise effectively and that it could preserve the spectral fidelity of the restored hyperspectral images. Specifically, the recovered spectral curves using the proposed method are visually more similar to the original image compared with the control methods; quantitative matrices, including the noise reduction ration and mean relative deviation, also showed that the PLDA-NAPCT produced less bias than the control methods. Furthermore, the PLDA-NAPCT method is sensor-independent, and it could be easily adapted for removing the noise from different sensors.
机译：高光谱图像（HSI）中噪声的存在会严重影响图像质量。噪声消除是高光谱信息提取之前要完成的最重要和最具挑战性的任务之一。尽管在减轻噪声的影响方面已经取得了许多进展，但是频带之间的高度相关性和噪声协方差的预定义结构等问题仍然使我们无法有效实施高光谱降噪。在这封信中，提出了一种新的算法，称为罚线性判别分析（PLDA）和经噪声调整的主成分变换（NAPCT）。 PLDA用于搜索最佳噪声协方差结构，而NAPCT用于去除噪声。 HJ-1A HSI和EO-1 Hyperion的测试结果表明，所提出的PLDA-NAPCT方法可以有效地去除噪声，并且可以保留恢复的高光谱图像的光谱保真度。具体而言，与控制方法相比，使用该方法恢复的光谱曲线在视觉上更类似于原始图像。定量矩阵，包括降噪比和平均相对偏差，也表明PLDA-NAPCT产生的偏差比控制方法小。此外，PLDA-NAPCT方法与传感器无关，并且可以轻松地用于消除来自不同传感器的噪声。

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  • 来源
    《IEEE Geoscience and Remote Sensing Letters》 |2017年第3期|359-363|共5页
  • 作者

    Ming Lu; Luojia Hu; Tianxiang Yue; Ziyue Chen; Bin Chen; Xiaoqiang Lu; Bing Xu;

  • 作者单位

    State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;

    State Key Lab of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China;

    State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;

    State Key Lab of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China;

    State Key Lab of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China;

    Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China;

    State Key Laboratory of Resources and Environment Information System, Institute of Geographical Science and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;

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

    Hyperspectral imaging; Noise reduction; Covariance matrices; Lakes; Linear discriminant analysis; Correlation;

    机译：高光谱成像;降噪;协方差矩阵;湖泊;线性判别分析;相关性;

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