Texture classification algorithm of wood hyper-spectral image based on multi-fractal spectra

Yan-hui TANG; Peng ZHAO; Cheng-kun WANG

doi:10.3788/YJYXS20193412.1182

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Texture classification algorithm of wood hyper-spectral image based on multi-fractal spectra

Image Processing | 更新时间：2020-07-16

- Texture classification algorithm of wood hyper-spectral image based on multi-fractal spectra
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 12, Pages: 1182-1190(2019)
- 作者机构：
  
  东北林业大学信息与计算机工程学院, 黑龙江哈尔滨 150040
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20193412.1182
  CLC： TP391
- Received：28 August 2019，
  
  Accepted：19 September 2019，
  
  Published：05 December 2019
- 稿件说明：
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Yan-hui TANG, Peng ZHAO, Cheng-kun WANG. Texture classification algorithm of wood hyper-spectral image based on multi-fractal spectra[J]. Chinese journal of liquid crystals and displays, 2019, 34(12): 1182-1190.
DOI：

Yan-hui TANG, Peng ZHAO, Cheng-kun WANG. Texture classification algorithm of wood hyper-spectral image based on multi-fractal spectra[J]. Chinese journal of liquid crystals and displays, 2019, 34(12): 1182-1190. DOI： 10.3788/YJYXS20193412.1182.

摘要

为了过滤木材高光谱图像中大量的冗余信息，提升应用图像纹理进行分类的准确率，本文采用基于多重分形理论的木材高光谱图像分类算法。首先利用不同的特征选择算法选取最具代表性的10个波段；随后根据不同的函数密度图像对所选取波段的图像求解其多重分形曲线，将选择出的多个波段所对应的多重分形曲线取平均，得到表示样本纹理特征的多重分形曲线；最后使用支持向量机和BP神经网络分类器对多重分形曲线进行分类。实验表明，相对熵（K-L散度）要好于自适应波段选择（ABS）提取的波段，多重分形算法提取的高光谱图像纹理特征要好于灰度共生矩阵，支持向量机算法的分类准确率和速度要优于BP神经网络，融合K-L散度、多重分形和支持向量机算法能够有效地提高木材高光谱图像的识别准确率，最高识别准确率达到了97.91%。

Abstract

In order to increase the accuracy classified by image texture to remove plenty of useless information in wood hyper-spectral images

the texture classification algorithms based on the multifractal theory were used in this paper. Firstly

the most representative ten bands were screened out using different Feature selection algorithms

and then the multi-fractal curves of the selected bands were obtained according to different function density images. The multi-fractal curves were averaged

which could represent the texture feature of certain sample. Finally

the curves were classified by the Support Vector Machine (SVM) and BP neural network classifier. The result shows that the bands screened by K-L divergence are superior to that screened by adaptive band selection (ABS)

the image's texture features extracted by multi-fractal algorithm are better than that extracted by the gray level co-occurrence matrix(GLCM)

and the classification accuracy and classification speed of SVM have an advantage over that used BP neural network. It can be concluded that the integrating K-L divergence

multi-fractal with SVM algorithm can dramatically increase the recognition classification

which can reach 97.91% in our work.

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references

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