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Proceedings Paper

Classification of spatial patterns using wavelets
Author(s): Thomas S. Moon
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Paper Abstract

The interpretation of airphotos relies heavily on the identification of textures and spatial patterns. The 2-D wavelet transform can be used to quantify simple patterns for automated classification of pixels in an image. The transform generates a set of images similar in format to a multispectral image deck, but based on spatial information localized about each pixel. The images in this 'wavelet' deck each correspond to an analysis of patterns in the original image at different spatial resolutions. For example, where one image in the wavelet deck depends on spatial variations within a 32 by 32 pixel area, the next image in the deck contains information on spatial features within a 16 by 16 pixel area. Individual pixels in the wavelet deck can be classified using the same classification and pattern recognition algorithms used with multispectral images. Classifications based on the wavelet deck of four airphoto samples using a minimum-distance to means algorithm and an artificial neural network are presented.

Paper Details

Date Published: 4 November 1996
PDF: 9 pages
Proc. SPIE 2818, Multispectral Imaging for Terrestrial Applications, (4 November 1996); doi: 10.1117/12.256093
Show Author Affiliations
Thomas S. Moon, Montana Tech (United States)


Published in SPIE Proceedings Vol. 2818:
Multispectral Imaging for Terrestrial Applications
Brian Huberty; Joan B. Lurie; Jule A. Caylor; Pol Coppin; Pierre C. Robert, Editor(s)

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