基于稀疏鉴别嵌入的高光谱遥感影像分类

黄鸿1; 杨媚1; 张满菊3

doi:10.3788/OPE.20132111.2922

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基于稀疏鉴别嵌入的高光谱遥感影像分类

信息科学 | 更新时间：2020-08-12

- 基于稀疏鉴别嵌入的高光谱遥感影像分类
- Hyperspectral Remote Sensing Image Classification Based on SDE
- 光学精密工程 2013年21卷第11期页码：2922-2930
- 作者机构：
  
  1. 重庆大学光电技术及系统教育部重点实验室，重庆 400030
  2. 重庆川仪自动化股份有限公司技术中心，重庆 401121
  3. 酒泉卫星发射中心，甘肃酒泉 735300
- 作者简介：
- 基金信息：
  
  多流形半监督学习及其在高光谱遥感影像分类中的应用();稀疏多流形学习及其在高光谱遥感影像分类中的应用();基于稀疏多流形学习的高光谱遥感影像分类()
- DOI：10.3788/OPE.20132111.2922
  中图分类号： TP751.1
- 收稿日期：2013-03-28，
  
  修回日期：2013-04-28，
  
  网络出版日期：2013-11-22，
  
  纸质出版日期：2013-11-15
- 稿件说明：
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黄鸿, 杨媚, 张满菊. 基于稀疏鉴别嵌入的高光谱遥感影像分类[J]. 光学精密工程, 2013,21(11): 2922-2930

HUANG Hong, YANG Mei, ZHANG Man-Ju. Hyperspectral Remote Sensing Image Classification Based on SDE[J]. Editorial Office of Optics and Precision Engineering, 2013,21(11): 2922-2930
黄鸿, 杨媚, 张满菊. 基于稀疏鉴别嵌入的高光谱遥感影像分类[J]. 光学精密工程, 2013,21(11): 2922-2930 DOI： 10.3788/OPE.20132111.2922.

HUANG Hong, YANG Mei, ZHANG Man-Ju. Hyperspectral Remote Sensing Image Classification Based on SDE[J]. Editorial Office of Optics and Precision Engineering, 2013,21(11): 2922-2930 DOI： 10.3788/OPE.20132111.2922.

摘要

稀疏保持投影(SPP)是一种基于l1图的新型降维算法，它利用样本间的稀疏重构关系建图，但是SPP为非监督算法，分类效果受到限制。针对此问题，本文提出了一种新的稀疏流形学习算法-稀疏鉴别嵌入(SDE)。该算法在利用样本的稀疏重构关系建图时引入了样本的类别信息，并通过优化目标函数来得到投影矩阵，使得不同类的数据点在低维嵌入空间中尽可能地分散开。SDE通过结合数据稀疏性及类间流形结构的优点，不仅保留样本间的稀疏重构关系，而且通过引入训练样本的类别信息实现稀疏鉴别特征提取，更有利于分类。在Urban和Washington DC Mall数据集上的实验结果表明：SDE算法比其他算法的分类性能有明显的提升，在每类随机选取16个训练样本的情况下，SDE算法的分类精度分别达到了73.47%和98.35%。

Abstract

Sparsity Preserving Projection(SPP) is a new algorithm for reducing dimensions of dataset based on a weighted graph( l1-Graph)

which reconstructs the weighted graph by the sparse relationship of train samples. However

SPP is an unsupervised learning method essentially

and it doesnt employ any prior knowledge of class to extract identification features. For this issue

a novel algorithm

Sparsity Discriminant Embedding (SDE) is proposed. Unlike SPP

the SDE adopts the class information of train samples when it constructs weighted graph of sparse reconstruction relationship. The projection matrix of the SDE is obtained via optimizing objective function and making different kinds of data points separate in the low-dimensional embedding space via a projection. By combining both interclass manifold structure and sparse property

the SDE keeps the sparse reconstructive relationships of dataset

and employs the class information of train samples to increase the classification rate. The experimental results obtained from operations on Urban and Washington DC Mall datasets show that the classification efficiency of the SDE has improved greatly as compared to those of other algorithms. The obtained classification accuracy has been 73.47% and 98.35%

respectively

when 16 samples of each class are randomly selected for training.

关键词

Keywords

references

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