Online multispectral image analysis using an unsupervised neural network

Thomas Taiwei Lu; Sizhe Tan; Rongqing Lu; Jeremy M. Lerner

doi:10.1117/12.310556

9 June 1998 Online multispectral image analysis using an unsupervised neural network

Thomas Taiwei Lu, Sizhe Tan, Rongqing Lu, Jeremy M. Lerner

Proceedings Volume 3261, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V; (1998) https://doi.org/10.1117/12.310556
Event: BiOS '98 International Biomedical Optics Symposium, 1998, San Jose, CA, United States

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.

Citation Download Citation

Thomas Taiwei Lu, Sizhe Tan, Rongqing Lu, and Jeremy M. Lerner "Online multispectral image analysis using an unsupervised neural network", Proc. SPIE 3261, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing V, (9 June 1998); https://doi.org/10.1117/12.310556

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