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ERDAS IMAGINEERDAS APPOLO USER FORUM Japan 2014 ২ᘐኰʼǻȟȊȸᲩȑȳǷȣȸȗȳೞᏡƷኰʼ ૨ሥဪӭᲴ25&#6&+25

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ERDAS IMAGINEERDAS APPOLO USER FORUM Japan 2014

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A number of models have been suggested to achieve this image merge. Welch and Ehlers (Welch and Ehlers, 1987) used forward-reverse RGB to HIS transforms, replacing I (from transformed TM data) with the SPOT panchromatic image. However, this technique is limited to three bands (R, G, B).

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If all of the above calculations are done in a mathematically rigorously way (histomatch and resample before substitution, and so forth) one can derive a multispectral image that has the high-pass (high-frequency) details from the 5-meter image.

In the above scenario, it should be noted that the high-resolution image (panchromatic, perhaps) is a single band and so the substitution image, from the multispectral image, must also be a single band. There are tools available to compress the multispectral image into a single band for substitution using the IHS transform or PC transform. Alternately, single bands can be processed sequentially.

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ERDAS IMAGINE 2011 also introduces Hyperspherical Color Space (HCS) pan sharpening, developed specifically for DigitalGlobe's WorldView-2 data. HCS pan sharpening accepts any number of bands and handles both spatial and spectral recovery over a wide variety of scenes.

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Chavez (Chavez et al, 1991), among others, uses the forward-reverse principal components transforms with the SPOT image, replacing PC-1. In the above two techniques, it is assumed that the intensity component (PC-1 or I) is spectrally equivalent to the SPOT panchromatic image, and that all the spectral information is contained in the other PCs or in H and S. Since SPOT data do not cover the full spectral range that TM data do, this assumption does not strictly hold. It is unacceptable to resample the thermalband (TM6) based on the visible (SPOT panchromatic) image.

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The algorithm is derived from the four component technique of Crippen (Crippen, 1989a). In this paper, it is argued that of the four possible arithmetic methods to incorporate an intensity image into a chromatic image (addition, subtraction, division, and multiplication), only multiplication is unlikely to distort the color. However, in his study Crippenfirst removed the intensity component via band ratios, spectral indices, or PC transform. The algorithm shown above operates on the original image. The result is an increased presence of the intensity component. For many applications, this is desirable. People involved in urban or suburban studies, city planning, and utilities routing often want roads and cultural features (which tend toward high reflection) to be pronounced in the image.

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Brovey Transform was developed to visually increase contrast in the low and high ends of an image’s histogram (that is, to provide contrast in shadows, water and high reflectance areas such as urban features). Consequently, the Brovey Transform should not be used if preserving the original scene radiometry is important. However, it is good for producing RGB images with a higher degree of contrast in the low and high ends of the image histogram and for producing visually appealing images. Since the Brovey Transform is intended to produce RGB images, only three bands at a time should be merged from the input multispectral scene, such as bands 3, 2, 1 from a SPOT or Landsat TM image or 4, 3, 2 from a Landsat TM image. The resulting merged image should then be displayed with bands 1, 2, 3 to RGB.

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