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Proceedings Paper

Neural network-based sharpening of Landsat thermal-band images
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Paper Abstract

Image sharpening based on neural network (NN) approximation techniques is applied to increase the spatial resolution of Landsat thematic mapper (TM) thermal-infrared (T-IR) data. Sharpening is derived from a learned input-output mapping of image edge contrast patterns between T-IR and higher resolution reflective TM bands. This method is similar to a reported adaptive least squares (LS) method used to estimate TM T-IR data at a higher resolution. However, there are two major differences: use of NN approximation instead of LS estimation, and application of a reported multiresolution technique to combine spatial information adaptively from the original image and its high spatial resolution estimate. With training pair examples from reduced spatial resolution data, a multilayer feedforward NN is trained to approximate T-IR data samples from a small neighborhood of samples from three other TM bands. Output of the trained NN for full-resolution input data is an estimate of T-IR image at full resolution. One advantage of this method is that the NN approximator can be trained from a subset of image scene samples and yet be applied to the entire scene. Preliminary examples illustrate sharpening at four times higher resolution. The accuracy of the technique was evaluated with a simulated lower spatial resolution image that included blurring introduced by the TM sensor's PSF. Although results are promising, further evaluation with simulated lower resolution IR data is needed.

Paper Details

Date Published: 6 July 1998
PDF: 9 pages
Proc. SPIE 3387, Visual Information Processing VII, (6 July 1998); doi: 10.1117/12.316428
Show Author Affiliations
George P. Lemeshewsky, U.S. Geological Survey (United States)

Published in SPIE Proceedings Vol. 3387:
Visual Information Processing VII
Stephen K. Park; Richard D. Juday, Editor(s)

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