Multiscale segmentation-based sparse coding for hyperspectral image classification

TANG Zhong-qi; FU Guang-yuan; CHEN Jin; ZHANG Li

doi:10.3788/OPE.20152309.2708

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Multiscale segmentation-based sparse coding for hyperspectral image classification

更新时间：2020-08-12

- Multiscale segmentation-based sparse coding for hyperspectral image classification
- Optics and Precision Engineering Vol. 23, Issue 9, Pages: 2708-2714(2015)
- 作者机构：
  
  1. 清华大学电子工程系北京,100084
  2. 北京市遥感信息研究所北京,100192
  3. 第二炮兵工程大学信息工程系,陕西西安,710025
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20152309.2708
  CLC： TP751
- Received：27 April 2015，
  
  Revised：01 June 2015，
  
  Published：25 September 2015
- 稿件说明：
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唐中奇, 付光远, 陈进等. 基于多尺度分割的高光谱图像稀疏表示与分类[J]. 光学精密工程, 2015,23(9): 2708-2714

TANG Zhong-qi, FU Guang-yuan, CHEN Jin etc. Multiscale segmentation-based sparse coding for hyperspectral image classification[J]. Editorial Office of Optics and Precision Engineering, 2015,23(9): 2708-2714
唐中奇, 付光远, 陈进等. 基于多尺度分割的高光谱图像稀疏表示与分类[J]. 光学精密工程, 2015,23(9): 2708-2714 DOI： 10.3788/OPE.20152309.2708.

TANG Zhong-qi, FU Guang-yuan, CHEN Jin etc. Multiscale segmentation-based sparse coding for hyperspectral image classification[J]. Editorial Office of Optics and Precision Engineering, 2015,23(9): 2708-2714 DOI： 10.3788/OPE.20152309.2708.

摘要

针对高光谱特征的稀疏表示

提出了一种基于多尺度分割的空间加权算法用于高光谱图像分类。该算法采用更合理的邻域定义挖掘空间先验信息

优化类边缘像元的稀疏表示。首先

通过多尺度分割提供邻域空间约束;结合拉普拉斯尺度混合(LSM)先验

分别对每个邻域组内像元进行空间加权的稀疏表示。然后

采用概率支持向量机(SVM)分类

同时提供像元的分类标签及其置信度。最后

以此置信度为权重

对多尺度分类图进行加权融合

生成最终的分类图。实验显示

本文算法能够增强光谱特征表示的稀疏性和鲁棒性

提高总体分类精度;在小样本训练下

单类的分类精度可提升30%左右

表明该算法在高光谱应用中具有较强的实用性。

Abstract

For the sparse representation of hyperspectral characteristics

a spatial weighted algorithm based on multiscale segmentation is proposed for hyperspectral classification. The algorithm uses a more reasonable neighborhood definition to mine spatial prior information to optimize the sparse representation of a like-edge pixel. Firstly

spatial neighborhoods were obtained through multiscale segmentation

and Laplacian Scale Mixture

(LSM) priori was then combined for the spatial-weighted sparse representation of pixels in each neighborhood. Then

the probabilistic Support Vector Machine(SVM) was used to classify the hyperspectral images and to provide classification labels and their confidences. Finally

the multiscale segmentation was weighted by the confidence of each label and the classification map was obtained by the fusion of labels. Experiments show that the algorithm enhances the sparse and roughness characterized by spectral features and improves the classification accuracy. Under smaller sample training

the classification accuracy of single ground surface has increased by 30%

which verifies the practicability of the proposed algorithm in hyperspectral applications.

关键词

Keywords

references

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