I am working on sparse recovery for a classification task. I use Pine hyperspectral dataset which is a freely available dataset and this image contains 200 Dimension (Depth/channels/bands). In addition, this dataset contains labels (Ground truth information). Thus I extract the corresponding pixels for each class and now I want to visualize them to see really how they look like. But I do know now how to visualize them for all 200 channels. I did this visualization for 3D but I want to know how can I expand this for 3D.
Here is my code for 3D, I would appreciate any help for visualizing my data in 3D Not: I simulate the dataset.
clear all % Number of classes = 5 B = [1 1 2; 2 3 4; 2 1 3; 4 3 4; 5 5 3]; B_vec = reshape(B, 5*3, 1); % Image with 200 depth A = rand(5,3, 200)*100; A_vec = reshape(A, 5*3, 200); %Indexing the desire classes. indx = find(or(B==1, B==4, B==3)); class_num = B_vec(indx); %Extracting the corresponding pixels (base on indx)from 200D image. for k = 1: length(indx) img_value(k,:) = A_vec(indx(k),:); end G_idx_1 = (find(class_num==1)); for k = 1:length(G_idx_1) C_1(k,:) = img_value(G_idx_1(k),:); end G_idx_3 = (find(class_num==3)); for k = 1:length(G_idx_3) C_3(k,:) = img_value(G_idx_3(k),:); end G_idx_4 = (find(class_num==4)); for k = 1:length(G_idx_4) C_4(k,:) = img_value(G_idx_4(k),:); end X1 = [C_1']; X2 = [C_3']; X3 = [C_4']; plot3(X1(1,:), X1(2,:), X1(3,:),'bo'); axis on; grid on; hold on plot3(X2(1,:), X2(2,:), X2(3,:),'go'); plot3(X3(1,:), X3(2,:), X3(3,:),'ro'); hold off; title('class1, class2, class3')