Share Email Print

Proceedings Paper

Unassisted reduction and segmentation of large hyperspectral image datasets
Author(s): Leanna N. Ergin; Venkata N. K. Rao Bobba; John F. Turner II
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The information density in hyperspectral data is not uniform across the spectral and spatial dimensions, and the overall information sparsity is often high. While these non-uniformities underpin the sought-after image contrast, high sparsity generates unnecessarily long acquisition and data processing times. Conventional reduction techniques like those based on principal components analysis (PCA) sacrifice the contributions of minority pixel populations while retaining those representing a greater portion of the overall variability. The effect is that some regions in the reconstructed images achieve a higher degree of recovery than other locations, making it difficult to assess the meaning or relevance of the minority pixels, even when this information would reveal important sample defects or spectral inhomogeneities. In the work presented here, we introduce a novel user-unassisted data reduction and image segmentation method called reduction of spectral images (ROSI). The aim of ROSI is to achieve a threshold information density in the spectral dimension for all image pixels. The result effectively segments the image in a manner that provides rapid image contrast that is comparable to traditionally classified images, but does so without a priori information. In addition, ROSI results are suitable for subsequent data analysis and enable ROSI to be performed alone or as a preprocessing data reduction step. A full description of ROSI is presented along with results from both model and real hyperspectral data, and its performance is compared quantitatively to conventional class of data reduction methods.

Paper Details

Date Published: 24 August 2017
PDF: 16 pages
Proc. SPIE 10395, Optics and Photonics for Information Processing XI, 103950M (24 August 2017); doi: 10.1117/12.2274451
Show Author Affiliations
Leanna N. Ergin, Cleveland State Univ. (United States)
Venkata N. K. Rao Bobba, Cleveland State Univ. (United States)
John F. Turner II, Cleveland State Univ. (United States)

Published in SPIE Proceedings Vol. 10395:
Optics and Photonics for Information Processing XI
Khan M. Iftekharuddin; Abdul A. S. Awwal; Mireya García Vázquez; Andrés Márquez; Víctor H. Diaz-Ramirez, Editor(s)

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