Because the image data is from the infrared part of the spectrum, you are technically free to “colorize” the data to highlight your regions of interest as you feel appropriate, without any concern that you must represent what the human eye would see. You may be interested in the spiral arms, the central core of the galaxy, or the dust lanes, for example.
To display the data in ENVI, you may wish to combine the three FITS image bands into a single image file that ENVI can read directly. Assuming I have read the other two downloaded files into the variables f1130w and f2100w, to create a (floating point) TIFF image use:
IDL> f = fltarr(3, 5648, 2092)
IDL> f[0, *, *] = f1000w
IDL> f[1, *, *] = f1130w
IDL> f2, *, *] = f2100w
IDL> write_tiff, 'ngc628.tif', reverse(f, 3),/float
We use the IDL “REVERSE” function here to flip the data in Y. The orientation of TIFF file is inverted relative to the default IDL array order. Next, open ENVI and simply import the TIFF file.
From here, you have access to all of ENVI’s built-in image processing tools such as data stretching and the ability to change the band order. And as a more compelling use case, you can now use ENVI’s more powerful data classification and analysis tools like ENVI Deep Learning to make novel discoveries in the data.
Jim Pendleton is a Professional Services Engineer at L3Harris Geospatial and former employee of the Space Telescope Science Institute.