Accessing Hyperspectral Images Using MATLAB
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I am doing a project on image fusion of Hyperspectral Images
It involves calculating the local variance for each band .
I downloaded Hyperspectral Images from this website --> http://personalpages.manchester.ac.uk/staff/david.foster/Hyperspectral_images_of_natural_scenes_02.html
But the images are in .mat format, and I am not able to access the data through Wavelet Toolbox .
What should I do so as to separate the image into various bands ?
How do i find the local variance for each image ?
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Brett Shoelson
2011-2-10
If the images are stored in .mat files, you should be able to LOAD them with core MATLAB--no special Wavelet-reading functions needed.
Then it's a matter of indexing. Assuming your spectral cube A is m x n x p (with p spectral bands), you would pick out the first band with A(:,:,1), and the pth band with A(:,:,p). (Et cetera.)
Brett
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S@M
2018-2-19
- %% Simple Load Hyperspectral data and Crossponding Ground Truths.
- load('Indian_pines_corrected.mat');
- load('Indian_pines_gt.mat');
- img = indian_pines_corrected;
- gt = indian_pines_gt; clear indian*
- %% Display individual Band.
- imagesc(img(:,:,i)); %% i could be any depending upon your choice raning from 1-224 in this case.
- imagesc(gt); %% Show Ground Truths.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/187903/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/187905/image.png)
- %% Hope this helps.
- %% Don't forget to read the related works.
- %% A New Statistical Approach for Band Clustering and Band Selection Using K-Means Clustering.
- %% AIK Method for Band Clustering Using Statistics of Correlation and Dispersion Matrix.
- %% Hyperspectral unmixing using statistics of Q function.
- %% unmixing and target detection of hyperspectral imagery using OSP.
- %% Metric similarity regularizer to enhance pixel similarity performance for hyperspectral unmixing.
- %% Unsupervised geometrical feature learning from hyperspectral data.
- %% Graph-based Spatial-Spectral Feature Learning for Hyperspectral Image Classification.
- %% Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.
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