Superpixel Tensor Sparse Coding for Structural Hyperspectral Image Classification

被引：12

作者：

Feng, Zhixi ^{[1
]}

Wang, Min ^{[1
]}

Yang, Shuyuan ^{[1
]}

Liu, Zhi ^{[1
]}

Liu, Linzan ^{[1
]}

Wu, Bin ^{[1
]}

Li, Hong ^{[2
]}

机构：

[1] Xidian Univ, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China

[2] XianYang Normal Univ, Xianyang 712000, Shaanxi, Peoples R China

来源：

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING | 2017年 / 10卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Hierarchical spatial neighborhood; hybrid pixel-superpixel ensemble; superpixel tensor sparse coding (STSC); COLLABORATIVE-REPRESENTATION; DIMENSIONALITY REDUCTION; SPATIAL CLASSIFICATION; SEGMENTATION; MODELS;

D O I：

10.1109/JSTARS.2016.2640449

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a superpixel tensor sparse coding (STSC) based hyperspectral image classification (HIC) method is proposed, by exploring the high-order structure of hyperspectral image and utilizing information along all dimensions to better understand data. First, a hierarchical spatial affinity propagation algorithm is developed to rapidly cluster the image into multiple superpixels tensors. Then, a new STSC-based classifier followed by hybrid pixel-superpixel ensemble strategy is constructed for HIC. Because superpixels can reduce the misclassification caused by mixed pixel and tensor sparse coding can simultaneously classify multiple superpixels, rapid and accurate HIC can be achieved. Some experiments are taken on several datasets, and the results show the superiority of STSC to its counterparts in terms of speed and accuracy.

引用

页码：1632 / 1639

页数：8

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