www.pudn.com > image_mva_0.rar > im_class_MLE.m

function class=im_class_MLE(im,plot); 
% function class=im_class_MLE(im,plot); 
% 
% routine for performing classification of multispectral images using 
% Maximum Likelihood Estimation algorithm 
% 
% the input is a single [n m] or multispectral image set [m n p] 
% 
% user is prompt to select ROIs from image that mostly describes different 
% classes 
%  
% the output is a classified map CLASS 
% 
% created by K.Artyushkova 
% 022004 
% 
% Kateryna Artyushkova 
% Postdoctoral Scientist 
% Department of Chemical and Nuclear Engineering 
% The University of New Mexico 
% (505) 277-0750 
% kartyush@unm.edu  
 
 
% the user is prompt to select the number of classes 
h = msgbox('You need to train the MLE classifier by specifying Regions of Interest on the images within areas mostly representing particular class'); 
uiwait(h) 
N = inputdlg('How many training ROIs you want to spesify?'); 
n=str2double(N); 
[a,b,c]=size(im); 
if c==1; 
% selection of roi from single image 
    for i=1:n; 
          [J,BW] = roifill(uint8(im)); 
          roi(:,:,i)=BW; 
      end 
else 
% selection of roi from multispectral images - have to choose the image to 
% draw ROIs on 
      R = inputdlg('Which image from the array to use for ROIs drawing?'); 
      R=str2double(R); 
      h = msgbox('Using mouse draw boxes on the image. These boxes will be used as traning regions'); 
      uiwait(h) 
    
      for i=1:n; 
          [J,BW] = roifill(uint8(im(:,:,R))); 
          roi(:,:,i)=BW; %BW is a binary image with 1's for pixels corresponding to the ROI and 0's elsewhere. 
      end 
  end 
close(figure(1)) 
h = waitbar(0,'Please wait while MLE classification is performed...'); 
   
for i=1:n; 
    [I,J] = find(roi(:,:,i)==1); %find indices of all pixels within ROIs 
    [m(i),p(i)]=size(I); 
    clear v 
    for j=1:m(i) 
    v(j,:)=im(I(j),J(j),:); % creating ROI matrices containing pixels within ROIs 
    end 
    M(i,:)=mean(v); % calculating mean vectors for each ROI matrix  
    siz = [m(i) 1];  
    clear M1 
    M1=M(i,:); 
    [p,q] = size(M1);  
    mind1 = (1:p)'; nind1 = (1:q)';  
    mind1 = mind1(:,ones(1,siz(1))); 
    nind1 = nind1(:,ones(1,siz(2))); 
    clear Me 
    Me = M1(mind1,nind1); % creating ROI mean matrix - each column has the same vector of means 
    clear Vc 
    Vc = v - Me; % Remove the mean matrix from the ROI matrix data 
    clear cov 
    cov = Vc' * Vc / (m(i)-1); % calculating covariance matrices for each ROI 
    Det(i)=det(cov); % calculating the determinant of the covariance matrix for each ROI 
    Inv(:,:,i)=(inv(cov)); %calculating invrse of covariance matrix for each ROI 
end 
 
% calculating discriminant function for each pixel 
for i1=1:a 
    for j1=1:b 
        x=im(i1,j1,:); 
        x=reshape(x,[1 q]); % extracting pixel value or vector 
        for i2=1:n; 
            g(i2) = - log(Det(i2))-(x'-M(i2,:)')' *Inv(:,:,i2)*(x'-M(i2,:)'); % ln(det(Cov)-(x-Mean)T*inv(Cov)*(x-Mean) 
        end 
        G=sum(g); % sum of all discriminant functions 
        for i=1:n 
            P(i)=g(i)/G; % calculating probability vector 
 
        end 
        [r,s]=max(P); % determining the max probbality - index s =class number 
 
        % Class assignment 
        for i=1:n 
            A(i)=250/(n-i+1); % claculating vector of colors to assing (equally spaced numbers between 0 and 250) 
            if s==i 
             class(i1,j1)=A(i); %class assignment 
         end 
        end          
     end 
 end 
close(h) 
% display classification results 
if plot==1 
H.Position=[421 330 369 337]; 
figure(H); 
imagesc(class,[0 260]) 
else 
end