matlab代码：有蓝、绿、红、近红外四个波段多光谱400*400的bmp遥感图象以及1600*1600的全色遥感，图象参照成分替换和多分辨率分析融合的方法，进行遥感图像融合

以下是Matlab代码实现：

% 读取多光谱图像
multispectral = imread('multispectral.bmp');

% 读取全色图像
panchromatic = imread('panchromatic.bmp');

% 将多光谱图像的四个波段分离，分别为蓝、绿、红、近红外
blue = multispectral(:,:,1);
green = multispectral(:,:,2);
red = multispectral(:,:,3);
nir = multispectral(:,:,4);

% 进行成分替换
% 将全色图像进行降采样，使其与多光谱图像的尺寸一致
downsampled_panchromatic = imresize(panchromatic, [400,400]);

% 将全色图像与红波段进行成分替换
substituted_red = downsampled_panchromatic;
substituted_green = green;
substituted_blue = blue;
substituted_nir = nir;

% 进行多分辨率分析融合
% 将四个波段进行小波变换
red_wt = wavedec2(substituted_red, 2, 'db4');
green_wt = wavedec2(substituted_green, 2, 'db4');
blue_wt = wavedec2(substituted_blue, 2, 'db4');
nir_wt = wavedec2(substituted_nir, 2, 'db4');

% 对低频分量进行加权平均融合
fused_low_freq = (red_wt{1} + green_wt{1} + blue_wt{1} + nir_wt{1})/4;

% 对高频分量进行基于能量的融合
fused_high_freq = zeros(size(red_wt{1}));
for i = 1:3
    red_energy = sum(red_wt{i+1}(:).^2);
    green_energy = sum(green_wt{i+1}(:).^2);
    blue_energy = sum(blue_wt{i+1}(:).^2);
    nir_energy = sum(nir_wt{i+1}(:).^2);
    energy_sum = red_energy + green_energy + blue_energy + nir_energy;
    fused_high_freq = fused_high_freq + (red_wt{i+1} + green_wt{i+1} + blue_wt{i+1} + nir_wt{i+1}) * (energy_sum/(4*(red_energy+green_energy+blue_energy+nir_energy)));
end

% 将低频分量和高频分量合并
fused_wt = {fused_low_freq, fused_high_freq, fused_high_freq, fused_high_freq};
fused_image = waverec2(fused_wt, 'db4');

% 显示融合后的图像
imshow(fused_image);

注：本代码使用小波变换进行多分辨率分析融合，其中使用了db4小波基函数。