多光谱图像融合 matlab.zip

function [F] = img_pca(AA,BB) imgPan = AA; imgMul = BB; imgPan=double(imgPan); R=double(imgMul(:,:,1)); G=double(imgMul(:,:,2)); B=double(imgMul(:,:,3)); [M,N]=size(R); up_Mx = 0; up_Cx = 0; for i = 1 : M for j = 1 : N S = [R(i,j),G(i,j),B(i,j)]'; % 生成由R，G， B组成的三维列向量 up_Mx = up_Mx + S; up_Cx = up_Cx + S*S'; end end up_Mx = up_Mx / (M*N); % 计算三维列向量的平均值 up_Cx = up_Cx / (M * N)- up_Mx*up_Mx'; % 计算协方差矩阵 [up_A,up_latent] = eigs(up_Cx); % 协方差矩阵的特征向量组成的矩阵----PCA变换的系数矩阵,特征值 P1=zeros(M,N);P2=zeros(M,N);P3=zeros(M,N); for i = 1 : M for j = 1 : N up_X = [R(i,j),G(i,j),B(i,j)]'; % 生成由R，G，B组成的三维列 up_Y = up_A'*up_X; % 每个像素点进行PCA变换正变换 up_Y = up_Y'; P1(i,j) = up_Y(1); % 多光谱图的第1主分量 P2(i,j) = up_Y(2); % 多光谱图的第2主分量 P3(i,j) = up_Y(3); % 多光谱图的第3主分量 end end % imshow(uint8(P1),[ ]); R1=zeros(M,N);R2=zeros(M,N);R3=zeros(M,N); for i = 1 : M for j = 1 : N up_Y = [imgPan(i,j),P2(i,j),P3(i,j)]'; % 生成由原全色图、多光谱图的第二分量和第三分量组成的三维列向量 up_X = up_A*up_Y; % 即用原全色图替换多光谱图的第一主成分 up_X = up_X'; % 每个像素点进行PCA变换反变换 R1(i,j) = up_X(1); R2(i,j) = up_X(2); R3(i,j) = up_X(3); end end RGB(:,:,1)=R1; RGB(:,:,2)=R2; RGB(:,:,3)=R3; F=uint8(RGB); end