Abstract: In the process of remote sensing images fusion, the spectral distortion of fusion image is the main problem. To reduce distortion, an optimization image fusion method in combination with entropy component analysis transform was proposed. First, multi-band image was transformed to a small amount of bands by the entropy component analysis to reduce the spectral dimension. Projection transformation was finished from the perspective of entropy contribution so as to keep more information of source bands. Wavelet decomposition was done between the first entropy component and the high resolution image after histogram matching to get low frequency and high frequency subbands. For the fusion of low frequency subbands, Quantum-behaved Particle Swarm Optimization (QPSO) algorithm was applied to select the optimal weight coefficients. For the high frequency subbands, statistical feature and statistical model were used to perform fusion. The result of wavelet fusion was regarded as the first entropy principal component. The fusion image was obtained by wavelet and entropy component inverse transform. Entropy, cross entropy, standard deviation, grad, correlation coefficient and spectral distortion were selected as objective evaluation indexes. The experimental results show that the proposed method can enhance the spatial information and avoid spectral distortion.

Key words: remote sensing image fusion, Principal Component Analysis (PCA), Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, statistical mo

摘要： 在遥感图像融合中，融合图像光谱失真是主要存在的问题，为此提出一种结合熵主成分变换与优化方法的图像融合方法。通过熵主成分变换将庞杂的多波段数据用尽可能少的波段表示出来，减少光谱维数，且从熵的贡献角度出发完成投影变换保留更多的源波段信息。取第一熵主分量，与直方图匹配后的全色图像进行小波变换，分别获取低频和高频子图。对低频子图采用量子粒子群优化方法搜索线性加权的最优融合权值，对高频子图采用统计特征与统计模型相结合的方式完成融合，小波融合结果作为第一熵主分量。最后，熵主成分逆变换得到融合后的遥感图像。选用熵、交叉熵、标准差、梯度、相关系数和光谱扭曲度作为客观评价指标。实验结果表明，所提方法能够提升空间细节且避免融合图像光谱失真。

关键词: 遥感图像融合, 主成分分析, 量子粒子群优化算法, 统计模型