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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Naive Gabor Networks for Hyperspectral Image Classification
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Naive Gabor Networks for Hyperspectral Image Classification

机译：天真的高光谱图像分类的Gabor网络

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摘要

Recently, many convolutional neural network (CNN) methods have been designed for hyperspectral image (HSI) classification since CNNs are able to produce good representations of data, which greatly benefits from a huge number of parameters. However, solving such a high-dimensional optimization problem often requires a large number of training samples in order to avoid overfitting. In addition, it is a typical nonconvex problem affected by many local minima and flat regions. To address these problems, in this article, we introduce the naive Gabor networks or Gabor-Nets that, for the first time in the literature, design and learn CNN kernels strictly in the form of Gabor filters, aiming to reduce the number of involved parameters and constrain the solution space and, hence, improve the performances of CNNs. Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase. With the phase-induced Gabor kernel, the proposed Gabor-Nets gains the ability to automatically adapt to the local harmonic characteristics of the HSI data and, thus, yields more representative harmonic features. Also, this kernel can fulfill the traditional complex-valued Gabor filtering in a real-valued manner, hence making Gabor-Nets easily perform in a usual CNN thread. We evaluated our newly developed Gabor-Nets on three well-known HSIs, suggesting that our proposed Gabor-Nets can significantly improve the performance of CNNs, particularly with a small training set.
机译：最近，许多卷积神经网络（CNN）方法专为高光谱图像（HSI）分类而设计，因为CNN能够产生良好的数据表示，这极大地受益于大量参数。然而，解决这样的高维优化问题通常需要大量的训练样本以避免过度拟合。此外，它是受许多当地最小值和平坦区域影响的典型的非核心问题。为了解决这些问题，在本文中，我们介绍了天真的Gabor网络或Gabor-net，这是严格地以Gabor滤波器的形式严格地设计和学习CNN内核，旨在减少涉及参数的数量并限制解决方案空间，从而提高CNN的性能。具体地，我们开发了一种创新的阶段诱导的Gabor内核，它被戏法设计用于通过核相位控制的局部低频和高频分量的线性组合来执行Gabor特征学习。通过相位诱导的Gabor内核，所提出的Gabor-Nets获得了自动适应HSI数据的局部谐波特性的能力，从而产生更多代表性的谐波特征。此外，这种内核可以以实值的方式满足传统的复杂的Gabor滤波，因此使Gabor网在通常的CNN螺纹中容易执行。我们在三个知名的HSIS上评估了我们新开发的Gabor网，这表明我们所提出的Gabor网可以显着提高CNN的性能，特别是小型培训集。

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  • 作者

    Chenying Liu; Jun Li; Lin He; Antonio Plaza; Shutao Li; Bo Li;

  • 作者单位

    Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation School of Geography and Planning Sun Yat-sen University Guangzhou China;

    Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation School of Geography and Planning Sun Yat-sen University Guangzhou China;

    School of Automation Science and Engineering South China University of Technology Guangzhou China;

    Hyperspectral Computing Laboratory Escuela Politécnica University of Extremadura Cáceres Spain;

    College of Electrical and Information Engineering Hunan University Changsha China;

    Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing China;

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

    Kernel; Feature extraction; Harmonic analysis; Training; Hyperspectral imaging; Convolution; Solid modeling;

    机译：内核;特征提取;谐波分析;训练;高光谱成像;卷积;实体建模;

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