Accessing Hyperspectral Images Using MATLAB
2 views (last 30 days)
Show older comments
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 ?
0 Comments
Accepted Answer
Brett Shoelson
on 10 Feb 2011
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
1 Comment
S@M
on 19 Feb 2018
- %% 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.
More Answers (0)
See Also
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!