Nonlinear Dimensionality Reduction of Hyperspectral Data Using Spectral Correlation as a Similarity Measure

被引：0
|
作者：
Myasnikov, Evgeny [1 ]
机构：
[1] Samara Univ, 34 Moskovskoye Shosse, Samara 443086, Russia
来源：
ANALYSIS OF IMAGES, SOCIAL NETWORKS AND TEXTS, AIST 2017 | 2018年 / 10716卷
基金：
俄罗斯基础研究基金会;
关键词：
Hyperspectral image; Spectral correlation Nonlinear dimensionality reduction; Nonlinear mapping Principal component analysis; IMAGERY;
D O I：
10.1007/978-3-319-73013-4_22
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we propose a novel dimensionality reduction method, which is based on the principle of preserving the pairwise spectral correlation measures. For the proposed method, we introduce the corresponding quality measure, and derive the numerical optimization algorithm based on a stochastic gradient descent technique. We provide the results of the experimental study that compares the method to the principal component analysis method using well-known hyperspectral scenes. The results of the study show that the proposed method can be successfully applied to process hyperspectral images.
引用
收藏
页码：237 / 244
页数：8
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