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Multispectral image visualization through first-order fusion
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AI
DOI：10.1109/TIP.2002.801588.png)
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Abstract
En 中文
We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first-order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first-order contrast of an image with an arbitrary number of bands. We demonstrate how our technique can reveal significantly more interpretive information to an image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed. A variety of experimental results are presented to support the performance of the new method.
Keywords:
multispectral imagery
remote sensing
visualization
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13.7
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1.0W
Citations:
8.4W
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