Hyperspectral Image Classification by Combination of Active Learning and Extended Multi-Attribute Profile

被引：0

作者：

Li, Changli ^{[1
]}

Zhang, Lin ^{[1
]}

机构：

[1] Hohai Univ, Coll Comp & Informat, Nanjing 211100, Jiangsu, Peoples R China

来源：

2016 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC) | 2016年

基金：

中国国家自然科学基金;

关键词：

hyperspectral image classification; spectral-spatial classification; active learning; extended multi-attribute profile; SUPPORT VECTOR MACHINES;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The main difficulty in hyperspectral image classification is the few labelled samples versus high dimensional features. Moreover, spatial information of hyperspectral image plays an important role. Therefore, we focus on combination of the active learning and extended multi-attribute profile spectral-spatial classification of hyperspectral image. We adopt active learning (AL) based on best versus second-best (BvSB) in order to iteratively select the most informative unlabeled samples and enlarge the training set. The spatial information is obtained by extended multi-attribute profile. To evaluate and compare the proposed approach with others, experiments were conducted on two hyperspectral data sets. Results demonstrated the effectiveness of the proposed method.

引用

页码：541 / 544

页数：4

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