Paper
6 July 1998 Neural network-based sharpening of Landsat thermal-band images
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
George P. Lemeshewsky "Neural network-based sharpening of Landsat thermal-band images", Proc. SPIE 3387, Visual Information Processing VII, (6 July 1998); https://doi.org/10.1117/12.316428
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Cited by 2 scholarly publications.
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KEYWORDS
Spatial resolution

Image resolution

Data analysis

Earth observing sensors

Image processing

Landsat

Neural networks

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