Progressive Band Selection Processing of Hyperspectral Image Classification

被引：13

作者：

Song, Meiping ^{[1
]}

Yu, Chunyan ^{[1
]}

Xie, Hongye ^{[1
]}

Chang, Chein-, I ^{[2
]}

机构：

[1] Dalian Maritime Univ, Informat & Technol Coll, Ctr Hyperspectral Imaging Remote Sensing CHIRS, Dalian 116026, Peoples R China

[2] Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Remote Sensing Signal & Image Proc Lab, Baltimore, MD 21250 USA

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2020年 / 17卷 / 10期

关键词：

Band selection (BS); class classification priority (CCP); hyperspectral image classification (HSIC); p-ary Huffman coding tree (HCT); progressive band selection processing of hyperspectral image classification (PBSP-HSIC);

D O I：

10.1109/LGRS.2019.2953525

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

This letter introduces a new approach to hyperspectral image classification (HSIC), called progressive band selection processing of hyperspectral image classification (PBSP-HSIC), which performs classification in multiple stages in the sense that each stage performs HSIC progressively according to a specifically selected band subset. Interestingly, such PBSP-HSIC offers a rare view of how different classes are classified in progressive stages, which has never been explored in the past. The experimental results also show that PBSP-HSIC performs better than HSIC using full bands.

引用

页码：1762 / 1766

页数：5

共 14 条

[1]

Chang C.-I., 2003, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, V1

[2] A Review of Virtual Dimensionality for Hyperspectral Imagery [J].