9 June 1998 Online multispectral image analysis using an unsupervised neural network
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas Taiwei Lu, Thomas Taiwei Lu, Sizhe Tan, Sizhe Tan, Rongqing Lu, Rongqing Lu, Jeremy M. Lerner, 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); doi: 10.1117/12.310556; https://doi.org/10.1117/12.310556
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