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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