create training and testing set with ground truth for Hyper spectral satellite imagery - matlab

Question

I am trying to create Training and Testing set out of my ground truth(observation) data which are presented in a tif (raster) format.
Actually, I have a hyperspectral image (Satellite image) which has 200 dimensions(channels/bands) along with the corresponding label(17 class) which are stored in another image. Now, my goal is to implement a classification algorithm and then check the accuracy with the testing dataset.
My problem is, that I do not know how can I describe to my algorithm that which pixel belongs to which class and then split them to taring and testing set.
I have provided a face idea of my goal which is as follows:
But I do not want to do this since I have 145 * 145 pixels dim, so it's not easy to define the location of these pixels and manually assign to their corresponding class.
note that the following example is for 3D image and I have 200D image and I have the labels (ground truth) so I do not need to specify them like the following code but I do want to assign them to their pixels member.
% Assigning pixel(by their location)to different groups.
tpix=[1309,640 ,1;... % Group 1
1218,755 ,1;...
1351,1409,2;... % Group 2
673 ,394 ,2;...
285 ,1762,3;... % Group 3
177 ,1542,3;...
538 ,1754,4;... % Group 4
432 ,1811,4;...
1417,2010,5;... % Group 5
163 ,1733,5;...
652 ,677 ,6;... % Group 6
864 ,1032,6];
row=tpix(:,1); % y-value
col=tpix(:,2); % x-value
group=tpix(:,3); % group number
ngroup=max(group);
% create trainingset
train=[];
for i=1:length(group)
train=[train; r(row(i),col(i)), g(row(i),col(i)), b(row(i),col(i))];
end %for

Answer 1

Do I understand this right? At the seconlast line the train variable gets the values it has until now + the pixels in red, green and blue? Like, you want them to be displayed only in red,green and blue? Only certain ones or all of them? I could imagine that we define an image matrix and then place the values in the images red, green and blue layers. Would that help? I'd make you the code if this is you issue :)
Edit: Solution
%download the .mats from the website and put them in folder of script
load 'Indian_pines_corrected.mat';
load 'Indian_pines_gt.mat';
ipc = indian_pines_corrected;
gt = indian_pines_gt;
%initiating cell
train = cell(16,1);
%loop to search class number of the x and y pixel coordinates
for c = 1:16
for i = 1:145
for j = 1:145
% if the classnumber is equal to the number in the gt pixel,
% then place the pixel from ipc(x,y,:) it in the train{classnumber}(x,y,:)
if gt(i,j) == c
train{c}(i,j,:) = ipc(i,j,:);
end %if
end %for j
end %for i
end %for c
Now you get the train cell that has a matrix in each cell. Each cell is one class and has only the pixels inside that you want. You can check for yourself if the classes correspond to the shape.

Answer 2

Eventually, I could solve my problem. The following code reshapes the matrix(Raster) to vector and then I index the Ground Truth data to find the corresponding pixel's location in Hyperspectral image.
Note that I am looking for an efficient way to construct Training and Testing set.
GT = indian_pines_gt;
data = indian_pines_corrected;
data_vec=reshape(data, 145*145,200);
GT_vec = reshape(GT,145*145,1);
[GT_vec_sort,idx] = sort(GT_vec);
%INDEXING.
index = find(and(GT_vec_sort>0,GT_vec_sort<=16));
classes_num = GT_vec_sort(index);
%length(index)
for k = 1: length(index)
classes(k,:) = data_vec(idx(index(k)),:);
end
figure(1)
plot(GT_vec_sort)
New.
I have done the following for creating Training and Testing set for #Hyperspectral images(Pine dataset). No need to use for loop
clear all
load('Indian_pines_corrected.mat');
load Indian_pines_gt.mat;
GT = indian_pines_gt;
data = indian_pines_corrected;
%Convert image from raster to vector.
data_vec = reshape(data, 145*145, 200);
%Provide location of the desired classes.
GT_loc = find(and(GT>0,GT<=16));
GT_class = GT(GT_loc)
data_value = data_vec(GT_loc,:)
% explanatories plus Respond variable.
%[200(variable/channel)+1(labels)= 201])
dat = [data_value, GT_class];
% create random Test and Training set.
[m,n] = size(dat);
P = 0.70 ;
idx = randperm(m);
Train = dat(idx(1:round(P*m)),:);
Test = dat(idx(round(P*m)+1:end),:);
X_train = Train(:,1:200); y_train = Train(:, 201);
X_test = Test(:,1:200); y_test = Test(:, 201);