High-efficiency Hyperspectral Unmixing Based on Band Selection

被引：1
|
作者：
Zhou, Yang [1 ]
Li, Xiaorun [1 ]
Cui, Jiantao [1 ]
机构：
[1] Zhejiang Univ, Coll Elect Engn, Hangzhou 310003, Zhejiang, Peoples R China
来源：
2012 THIRD GLOBAL CONGRESS ON INTELLIGENT SYSTEMS (GCIS 2012) | 2012年
关键词：
hyperspectral unmixing; band selection; nonnegative matrix factorization; ALGORITHM;
D O I：
10.1109/GCIS.2012.39
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Hyperspectral unmixing (HU) is important for ground objects identification. Due to the mass data hyperspectral sensors bring, band selection plays an important role in boosting efficiency of HU. This paper proposes a high-efficiency approach of HU that carries out two modified algorithms of band selection followed by nonnegative matrix factorization (NMF), which are linear prediction (LP) combined with K-L divergence and mutual information (MI). Experiment results based on simulated data and real hyperspectral imagery demonstrate that the proposed scheme is more efficient than initial NMF in HU.
引用
收藏
页码：140 / 143
页数：4
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