Share Email Print
cover

Proceedings Paper

Dimensionality reduction using superpixel segmentation for hyperspectral unmixing using the cNMF
Author(s): Jiarui Yi; Miguel Velez-Reyes
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

This paper presents an approach to reduce dimensionality for hyperspectral unmixing using superpixel segmentation. The dimensionality reduction is achieved by over-segmenting the hyperspectral image using superpixels that are used as a reduced subset of representative pixels for the full hyperspectral image. Once superpixel are extracted, endmember extraction methods are applied to the reduced spectral data set with clear computational advantages. The proposed method is illustrated on the AVIRIS image captured over Fort AP Hill, Virginia. A comparison of the method with standard unmixing techniques is also included.

Paper Details

Date Published: 5 May 2017
PDF: 8 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101981H (5 May 2017); doi: 10.1117/12.2264345
Show Author Affiliations
Jiarui Yi, The Univ. of Texas at El Paso (United States)
Miguel Velez-Reyes, The Univ. of Texas at El Paso (United States)


Published in SPIE Proceedings Vol. 10198:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray