Extracting features from a hyperspectral image

Hi, could anyone please recommend a step-by-step tutorial for an absolute beginner to go from image to data? I have hundreds of NIR images and want to extract data out to start the modelling etc., but having no experience with imaging data I am clueless. I assume there would be guided tutorials with instructions on how to go from .raw and .hdr to the stage where you have numbers in an output file to start testing. Any recommendataions are greatly appreciated.

Answer 1

[Image Analyst]

You're asking too much. We can't give you a lesson-by-lesson course on hyperspectral imaging. Try searching online for one.

However to get started with the basics of image processing in MATLAB, see my Image Segmentation Tutorial in my File Exchange:

Image Analyst's File Exchange

It's a generic, general purpose demo of how to threshold an image to find blobs, and then measure things about the blobs, and extract certain blobs based on their areas or diameters.

[Image Analyst]

I do have one hyperspectral demo. For what it's worth, it's attached.

[Luqman Safdar]

Thank you for your help, I will practice following your tutorials and see where it takes me. Sorry if it sounded too much but as I am new to this field and online searches returned hundreds of different tutorials but it was confusing to decide a good starting point. Also the purpose for me was only to extract data out of the images I have so I can continue with rest of the experiments (mostly plant genetics). Thank you again for your suggestions. If you have any good reading suggestions even involving other platforms (like python, R etc.) please do recommend. Cheers!

[Image Analyst]

I'd recommend staying with MATLAB. I took a week long Python class and didn't see any reason for me to move to that language. MATLAB could do everything I need to do, and was easier to do it in. Python is very fussy about indenting and having to have the right files "imported" and you don't have to worry about that in MATLAB.

I don't know what you want to extract -- you didn't upload an image and tell me so I can't make any suggestions.

[Luqman Safdar]

Sorry I didn't want to sound too demanding, but here is a screenshot (HSI image) of an image from hyperspectral viewer [uploading the actual file is difficult as it is >250 Mb in *.dat format]. Each image has 12 grains (however this only shows 10, an old file) in it and I want to extract features/absorbance of each grain separately, and the total images are over 500. The grain were placed on a black background. The second screenshot (HSI file types) shows the type of files I have in each folder. Any suggestions would be greatly appreciated.

[Image Analyst]

Is the background a spectrally flat white reference? Is there a wavelength that the grains all show up in? If so, use that for segmenting (finding the grain pixels as a binary image mask). Then you can get the average reflectance for each grain in that wavelength. Then you can use the mask to get the reflectance at each of the other wavelengths.

[Luqman Safdar]

Thanks again. The background was black as they were put on a black tray; however, I have white references as well. And the grains all show up around 110-125 (out of 256 bands) but different for different grains.

[Image Analyst]

So I'd like I said, create a mask by scanning through all 256 images thresholding just above the black background and using bwareaopen to take only blobs as big as, say half a grain. Then OR all the masks together to create a master mask with all grains in it. Then you can scan through again getting the mean intensity of all grains with regionprops