空-谱协同正则化稀疏超图嵌入的高光谱图像分类

doi:10.11947/j.AGCS.2019.20180469

测绘学报 ›› 2019, Vol. 48 ›› Issue (6): 676-687.doi: 10.11947/j.AGCS.2019.20180469

空-谱协同正则化稀疏超图嵌入的高光谱图像分类

黄鸿, 陈美利, 王丽华, 李政英

重庆大学光电技术与系统教育部重点实验室, 重庆 400044

收稿日期:2018-10-15 修回日期:2019-03-21 出版日期:2019-06-20 发布日期:2019-07-09
作者简介:黄鸿(1980-),男,教授,博士生导师,研究方向为流形学习、模式识别、遥感影像智能化处理。E-mail:hhuang@cqu.edu.cn
基金资助:
重庆市基础研究与前沿探索项目（cstc2018jcyjAX0093）；重庆市研究生科研创新项目（CYB18048；CYS18035）

Using spatial-spectral regularized hypergraph embedding for hyperspectral image classification

HUANG Hong, CHEN Meili, WANG Lihua, LI Zhengying

Key Laboratory of Optoelectronic Technique System of the Ministry of Education, Chongqing University, Chongqing 400044, China

Received:2018-10-15 Revised:2019-03-21 Online:2019-06-20 Published:2019-07-09
Supported by:
The Basic and Frontier Research Programmes of Chongqing (No. cstc2018jcyjAX0093);The Chongqing University Postgraduates Innovation Project (Nos. CYB18048;CYS18035)

摘要/Abstract

摘要： 传统依据图嵌入的高光谱图像维数约简算法多数仅利用光谱信息表征像元间单一关系，忽视了数据间的多元几何结构。本文提出了一种面向高光谱图像分类的空-谱协同正则化稀疏超图嵌入算法（SSRSHE）。该算法首先利用稀疏表示揭示像元之间的相关性，自适应选择近邻，并构建稀疏本征超图和惩罚超图，以有效表征像元间的复杂多元关系，并进行正则化处理。然后利用遥感图像空间一致性原则，计算局部空间邻域散度来保持样本局部邻域结构，并引入样本总体散度来保持高光谱数据的整体结构。在低维嵌入空间中，尽可能使类内数据聚集、类间数据远离，提取鉴别特征用于分类。在Indian Pines和PaviaU高光谱遥感数据集上试验结果表明，本文算法总体分类精度分别达到86.7%和92.2%。相比传统光谱维数约简算法，该算法可有效改善高光谱图像地物分类性能。

关键词: 高光谱图像, 维数约简, 正则化稀疏超图模型, 空-谱联合, 分类

Abstract: In recent years, many graph embedding methods were developed for dimensionality reduction (DR) of hyperspectral image (HSI), while these methods only use spectral information to reveal a simple intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRSHE) is proposed for the HSI classification. SSRSHE explores sparse coefficients to adaptively select neighbors for constructing the regularized sparse intrinsic hypergraph and the regularized sparse penalty hypergraph. Based on the spatial consistency property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed for global structure of HSI. Then, the optimal discriminant projection is obtained by possessing better intrinsic data compactness and penalty pixels separability, which is beneficial for classification. The experimental results on Indian Pines and PaviaU hyperspectral data sets show that the overall classification accuracies respectively reach 86.7% and 92.2%. The proposed SSRSHE method can effectively improve classification performance compared with the traditional spectral DR algorithms.

Key words: hyperspectral image, dimensionality reduction, regularized sparse hypergraph, spatial-spectral features, image classification

中图分类号:

TP751

黄鸿, 陈美利, 王丽华, 李政英. 空-谱协同正则化稀疏超图嵌入的高光谱图像分类[J]. 测绘学报, 2019, 48(6): 676-687.

HUANG Hong, CHEN Meili, WANG Lihua, LI Zhengying. Using spatial-spectral regularized hypergraph embedding for hyperspectral image classification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(6): 676-687.

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空-谱协同正则化稀疏超图嵌入的高光谱图像分类

Using spatial-spectral regularized hypergraph embedding for hyperspectral image classification

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