Dimensionality reduction of hyperspectral imagery using improved locally linear embedding

被引：37

作者：

Chen, Guangyi ^{[1
]}

Qian, Shen-En ^{[1
]}

机构：

[1] Canadian Space Agcy, St Hubert, PQ J3Y 8Y9, Canada

来源：

JOURNAL OF APPLIED REMOTE SENSING | 2007年 / 1卷

基金：

加拿大自然科学与工程研究理事会;

关键词：

Dimensionality reduction; remote sensing; locally linear embedding; hyperspectral; endmember; detection;

D O I：

10.1117/1.2723663

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In this paper, we study the Locally Linear Embedding (LLE) for nonlinear dimensionality reduction of hyperspectral data. We improve the existing LLE in terms of both computational complexity and memory consumption by introducing a spatial neighbourhood window for calculating the k nearest neighbours. The improved LLE can process larger hyperspectral images than the existing LLE and it is also faster. We conducted experiments of endmember extraction to assess the effectiveness of the dimensionality reduction methods. Experimental results show that the improved LLE is better than PCA and the existing LLE in identifying endmembers. It finds more endmembers than PCA and the existing LLE when the Pixel Purity Index (PPI) based endmember extraction method is used. Also, better results are obtained for detection.

引用

页数：10

共 23 条

[1]

Boardman J.W., 1994, P 10 THEM C GEOL REM, P407

[2]

BOARDMANN J, 1995, P SUMM 5 ANN JPL AIR

[3] Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction [J].