I am completely unfamiliar with AVIRIS data and its processing, so there may be much simpler or better methods of accessing it of which I am unaware. However, I found and downloaded a smallish sample from the linked website as follows:
Reading the .hdr file (which is ASCII fortunately), I was able to work out that the data are signed 16-bit integers, band-interleaved-by-pixel of 224 bands, and 735 samples/line and 2017 lines. So, I can then load the image and process it with Numpy as follows:
import numpy as np
from PIL import Image
# Define datafile parameters
channels, samples, lines = 224, 735, 2017
# Load file and reshape
im = np.fromfile('f090710t01p00r11rdn_b_ort_img', dtype=np.int16).reshape(lines,samples,channels)
The data are signed integers in the range -32768...+32767, so if we add 32768 the data will be 0..65536 and then multiply by 255/65535 we should get a viewable, but not radiometrically correct, image to prove the reading from file:
# That's kind of all - just do crude scaling to show we have it correctly
a = (im.astype(np.float)+32768.0)*255.0/65535.0
Now select band 0, and save (using PIL, but we could use OpenCV or tifffile):
Image.fromarray(a[:,:,0].astype(np.uint8)).save('result.png')
Presumably you can now arrange the data however you like with Numpy as we have read it successfully. So, say you want line 7 of band 4, you could use:
a[7,:,4]
Or line 0, all bands:
a[0,:,:]
If you wanted to make a false colour composite from 3 of the 224 bands, you can use np.dstack() like this - my bands were chosen at random:
FalseColour = np.dstack((a[...,37], a[...,164], a[...,200])).astype(np.uint8)
Image.fromarray(FalseColour).save('result.png')
Keywords: Python, AVIRIS, hyperspectral image processing, hyper-spectral, band-interleaved by pixel, by line, planar, interlace.
