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Pansharpening is a process of merging high-resolution panchromatic and lower resolution multispectral imagery to create a single high-resolution color image.[1] Google Maps and nearly every map creating company use this technique to increase image quality. Pansharpening produces a high-resolution color image from three, four or more low-resolution multispectral satellite bands plus a corresponding high-resolution panchromatic band:
Low-res color bands + High-res grayscale band = High-res color image
Such band combinations are commonly bundled in satellite data sets, for example Landsat 7, which includes six 30 m resolution multispectral bands, a 60 m thermal infrared band plus a 15 m resolution panchromatic band. SPOT, GeoEye and Maxar commercial data packages also commonly include both lower-resolution multispectral bands and a single panchromatic band. One of the principal reasons for configuring satellite sensors this way is to keep satellite weight, cost, bandwidth and complexity down. Pan sharpening uses spatial information in the high-resolution grayscale band and color information in the multispectral bands to create a high-resolution color image, essentially increasing the resolution of the color information in the data set to match that of the panchromatic band.
One common class of algorithms for pansharpening is called “component substitution,”[2] which usually involves the following steps:
Common color-space transformation used for pan sharpening are HSI (hue-saturation-intensity), and YCbCr. The same steps can also be performed using wavelet decomposition or PCA and replacing the first component with the pan band.
Pan-sharpening techniques can result in spectral distortions when pan sharpening satellite images as a result of the nature of the panchromatic band. The Landsat panchromatic band for example is not sensitive to blue light. As a result, the spectral characteristics of the raw pansharpened color image may not exactly match those of the corresponding low-resolution RGB image, resulting in altered color tones. This has resulted in the development of many algorithms that attempt to reduce this spectral distortion and to produce visually pleasing images.
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