- %% 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/179158/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/179159/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.