【图像配准】基于DLSC算法实现SAR图像配准matlab源码

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clear;
% % high resolution optical and sar matching
%  im_Ref = imread('.\data\optical_ref.png');
%  im_Sen = imread('.\data\SAR_sen.png');
%  CP_Check_file = '.\data\OpticaltoSAR_CP.txt';

% medium resolution optical and sar matching
 im_Ref = imread('.\data\Optical_ref2.tif');
 im_Sen = imread('.\data\SAR_sen2.tif');
 CP_Check_file = '.\data\OpticaltoSAR2_CP.txt';


%lidar intensity and optical matching
% im_Ref = imread('.\data\LiDARintensity_ref.tif');
% im_Sen = imread('.\data\optical_sen.tif');
% CP_Check_file = '.\data\LiDARtoOptical_CP.txt';


 %visible and infrared image matching
%im_Ref = imread('.\data\visible_ref.tif');
%im_Sen = imread('.\data\infrared_sen.tif');
%CP_Check_file = '.\data\VisibletoInfrared_CP.txt';


disthre = 1.5;        % the threshod of match errors the deflaut is 1.5. for
                      % high resolution image covering urban areas, we
                      % should set it to a larger threshod (such as 2.0).
                      % This is beccause that the geometric distortions between such images
                      % is very complicated, and the transfrom model used
                      % by us (such as projective model) can only prefit
                      % the geometric distortion. Therefore, a larger threshod
                      % have the more flexibility  

% template matching using DLSC
[CP_Ref,CP_Sen,CMR] = DLSC_match(im_Ref,im_Sen,CP_Check_file,disthre);
x= sprintf('the correct match ratio is %4.3f',CMR);
disp(x)

%diplay the tie points 
figure;
imshow(im_Ref),hold on;
plot(CP_Ref(:,1),CP_Ref(:,2),'yo','MarkerEdgeColor','k','MarkerFaceColor','y','MarkerSize',5);hold on;
title('reference image');

figure;
imshow(im_Sen),hold on;
plot(CP_Sen(:,1),CP_Sen(:,2),'yo','MarkerEdgeColor','k','MarkerFaceColor','y','MarkerSize',5);hold on;
title('sensed image');

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