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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Hyperspectral Unmixing via Deep Convolutional Neural Networks
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Hyperspectral Unmixing via Deep Convolutional Neural Networks

机译：深度卷积神经网络的高光谱解混

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摘要

Hyperspectral unmixing (HU) is a method used to estimate the fractional abundances corresponding to endmembers in each of the mixed pixels in the hyperspectral remote sensing image. In recent times, deep learning has been recognized as an effective technique for hyperspectral image classification. In this letter, an end-to-end HU method is proposed based on the convolutional neural network (CNN). The proposed method uses a CNN architecture that consists of two stages: the first stage extracts features and the second stage performs the mapping from the extracted features to obtain the abundance percentages. Furthermore, a pixel-based CNN and cube-based CNN, which can improve the accuracy of HU, are presented in this letter. More importantly, we also use dropout to avoid overfitting. The evaluation of the complete performance is carried out on two hyperspectral data sets: Jasper Ridge and Urban. Compared with that of the existing method, our results show significantly higher accuracy.
机译：高光谱解混（HU）是一种用于估计与高光谱遥感图像中每个混合像素中的末端成员相对应的分数丰度的方法。近年来，深度学习已被认为是用于高光谱图像分类的有效技术。在这封信中，提出了一种基于卷积神经网络（CNN）的端到端HU方法。所提出的方法使用的CNN架构包括两个阶段：第一阶段提取特征，第二阶段从提取的特征执行映射以获得丰度百分比。此外，在这封信中介绍了可以提高HU准确性的基于像素的CNN和基于立方体的CNN。更重要的是，我们还使用辍学来避免过拟合。完整性能的评估是在两个高光谱数据集上进行的：Jasper Ridge和Urban。与现有方法相比，我们的结果显示出更高的准确性。

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  • 来源
    《IEEE Geoscience and Remote Sensing Letters》 |2018年第11期|1755-1759|共5页
  • 作者

    Xiangrong Zhang; Yujia Sun; Jingyan Zhang; Peng Wu; Licheng Jiao;

  • 作者单位

    Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China;

    Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China;

    Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China;

    Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China;

    Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China;

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

    Feature extraction; Hyperspectral imaging; Convolution; Artificial neural networks; Indexes; Kernel;

    机译：特征提取高光谱成像卷积人工神经网络索引核;

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