Share Email Print

Proceedings Paper

Online multispectral image analysis using an unsupervised neural network
Author(s): Thomas Taiwei Lu; Sizhe Tan; Rongqing Lu; Jeremy M. Lerner
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The overwhelming size of a hyperspectral image creates serious problem for users to understand and utilize the data. A novel unsupervised neural network (UNN) model is presented. The UNN is designed to analyze the spectral contents of the multi-spectral images. The UNN automatically grows its layers and neurons by scanning the training images and by learning spectral features. The learning strategy is optimized to ensure fast convergence. At the end of learning, the UNN provides a table of the spectral content of the images. The image contents are categorized based on spectral similarities. The resulting spectral classes are then mapped onto the image, thus the UNN effectively compresses a hyperspectral image cube into a single image. The UNN is also able to automatically recognize objects by their spectral features.the ability of the UNN in identifying subtle spectral differences is shown.

Paper Details

Date Published: 9 June 1998
PDF: 9 pages
Proc. SPIE 3261, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V, (9 June 1998); doi: 10.1117/12.310556
Show Author Affiliations
Thomas Taiwei Lu, Photonics Research (United States)
Sizhe Tan, In-Harmony Technology Corp. (United States)
Rongqing Lu, In-Harmony Technology Corp. (United States)
Jeremy M. Lerner, LightForm, Inc. (United States)

Published in SPIE Proceedings Vol. 3261:
Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V
Thomas Taiwei Lu; Carol J. Cogswell; Jeremy M. Lerner; Jose-Angel Conchello; Jeremy M. Lerner; Thomas Taiwei Lu; Tony Wilson, Editor(s)

© SPIE. Terms of Use
Back to Top