1 简介
In this paper, a novel image fusion method for remote sensing applications is proposed. In order to estimate the primitive detail map, the band coefficients of multispectral images are computed using least squares method. To refine the detail map and better image fusion, an adaptive method is proposed to compute the weights of linear combinations of panchromatic (Pan) and multispectral (MS) gradients. The injected weights are calculated using particle swarm optimization (PSO). Two data sets obtained by WorldView-3 and QuickBird satellites are employed for testing the assessment and comparing with state-of-art methods.
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2 部分代码
%% This code is used to estimate the weights of the MultiSpectral (MS) bands based on Adaptive IHS (AIHS) method.%% References
% [1] S. Rahmani, M. Strait, D. Merkurjev, M. Moeller, and T. Wittman,
% M - Low Resolution MS (LRMS) image to size of PANchromatic (PAN) image
% P - Original PAN image
%% In the outputs you can find the estimated weight for each MS band
% findalph - Spectral weights
function findalph = impGradDes(M, P)
[n, m, d] = size(M);
%% Initializing the optimal weight vector
findalph = ones(d,1);
%% Optimization process
for i=1:d
for j=1:d
A(i,j) = sum(sum(M(:,:,i).*M(:,:,j)));
end
B(i,1) = sum(sum(P.*M(:,:,i)));
end
tau = 5;
iter = 150000;
gamma1 = 1/200000;
gamma2 = 1;
inv = (eye(d) + 2*tau*gamma1*A)^(-1);
for i = 1:iter
findalph = inv * (findalph+2*tau*max(-findalph,0)+2*tau*gamma1*B);
end
%% EOF
3 仿真结果
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4 参考文献
A. Azarang and H. Ghassemian, "An adaptive multispectral image fusion using particle swarm optimization," 2017 Iranian Conference on Electrical Engineering (ICEE), 2017, pp. 1708-1712, doi: 10.1109/IranianCEE.2017.7985325.
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部分理论引用网络文献，若有侵权联系博主删除。
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