Hyperspectral images feature extraction and classification based on fractional differentiation

LIU Jing; LI Yang; LIU Yi

doi:10.37188/OPE.20233121.3221

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Hyperspectral images feature extraction and classification based on fractional differentiation

Information Sciences | 更新时间：2023-11-16

- Hyperspectral images feature extraction and classification based on fractional differentiation
- Optics and Precision Engineering Vol. 31, Issue 21, Pages: 3221-3236(2023)
- 作者机构：
  
  1.西安邮电大学电子工程学院，陕西西安710121
  2.西安电子科技大学电子工程学院，陕西西安710071
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233121.3221
  CLC：
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刘敬,李洋,刘逸.基于分数阶微分的高光谱图像特征提取与分类[J].光学精密工程,2023,31(21):3221-3236.

LIU Jing,LI Yang,LIU Yi.Hyperspectral images feature extraction and classification based on fractional differentiation[J].Optics and Precision Engineering,2023,31(21):3221-3236.
刘敬,李洋,刘逸.基于分数阶微分的高光谱图像特征提取与分类[J].光学精密工程,2023,31(21):3221-3236. DOI： 10.37188/OPE.20233121.3221.

LIU Jing,LI Yang,LIU Yi.Hyperspectral images feature extraction and classification based on fractional differentiation[J].Optics and Precision Engineering,2023,31(21):3221-3236. DOI： 10.37188/OPE.20233121.3221.

摘要

针对高光谱遥感图像的特征提取与地物分类，提出一种基于分数阶微分的高光谱图像特征提取方法，设计二维分数阶微分掩模提取高光谱图像的像素空间分数阶微分（SpaFD）特征，并提出一种空谱联合准则用于选取微分掩模阶数。为充分利用高光谱图像的空间特征与光谱特征，将SpaFD特征与原始特征直连融合获得SpaFD-Spe-Spa混合特征，并采用三维卷积神经网络（3DCNN）、先采用主成分分析（PCA）对像素光谱进行降维处理再送入三维卷积神经网络（3DCNN,PCA,）以及采用混合光谱网络（HybridSN）验证SpaFD-Spe-Spa混合特征的有效性。实验中分别采用3✕3，5✕5和7✕7的分数阶微分掩模进行空间特征提取，4个真实高光谱图像的实验结果表明，所提取的SpaFD特征和SpaFD-Spe-Spa特征可有效提升高光谱图像的地物分类精度，且SpaFD-Spe-Spa特征对地物分类准确率的提升更为明显：SpaFD特征相比原始特征在Indian Pines，Botswana，Pavia University和Salinas 4个数据上的分类识别率在最优情况下分别提升了3.87%，1.42%，2.41%和2.87%；SpaFD-Spe-Spa特征相比原始特征在Indian Pines，Botswana，Pavia University和Salinas 4个数据上的分类识别率在最优情况下分别提升了3.90%，5.62%，3.35%和5.18%。

Abstract

Herein， a feature extraction method based on fractional differentiation is proposed for the feature extraction and classification of hyperspectral images. Two-dimensional （2D） fractional differential masks are designed to extract the pixel spatial fractional differential （SpaFD） feature of hyperspectral images， and a spectral–spatial joint criterion is proposed to select the differential mask order. To entirely utilize the spatial and spectral features of hyperspectral images， the SpaFD feature is fused with the original feature via a direct connection to obtain a mixed feature （SpaFD-Spe-Spa）. The effectiveness of the SpaFD-Spe-Spa feature is verified on a 3D convolutional neural network （3DCNN）， 3DCNN after pixel spectrum dimensionality reduction using principal component analysis （3DCNNPCA）， and hybrid spectral network （HybridSN）. In the experiment， masks with sizes of 3×3， 5×5， and 7×7 are used to perform feature extraction. Experiments on four real hyperspectral image datasets reveal that the extracted SpaFD and SpaFD-Spe-Spa features are effective in hyperspectral image classification， and the SpaFD-Spe-Spa feature significantly improves classification accuracy. When compared with the original features in the Indian Pines， Botswana， Pavia University， and Salinas datasets， the classification accuracy of the SpaFD feature is improved by 3.87%， 1.42%， 2.41%， and 2.87%， respectively， whereas that of the SpaFD-Spe-Spa feature is improved by 3.90%， 5.62%， 3.35%， and 5.18%， respectively， under optimal conditions.

关键词

高光谱图像分类分数阶微分特征提取卷积神经网络

Keywords

hyperspectral images classificationfractional differentiationfeature extractionconvolutional neural network

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