基于梯度一致性约束的多光谱/全色影像最大后验融合方法

多光谱/全色影像融合可以得到高空间分辨率的多光谱影像，在影像解译和分类等方面具有十分重要的意义。提出一种基于梯度一致性约束的遥感影像融合方法。该方法在最大后验概率框架下，通过梯度一致性约束建立理想高空间分辨率多光谱影像和全色影像之间的关系，并结合多光谱影像观测模型和Huber-Markov影像先验，构建融合目标函数，最后采用梯度下降法求解得到融合影像。本文方法在目标函数中引入了梯度一致性约束，克服了现有的同类方法受限于波段数量的缺陷，同时在求解中自适应确定每个波段的迭代步长，充分顾及了各波段的光谱特性，从而既确保了融合影像的光谱信息保真度，也提高了融合影像的空间信息融入度。通过IKONOS和WorldView-2影像对该方法进行了验证，并和GS，AIHS和AMBF等融合方法从定性和定量两方面进行了比较分析。实验结果表明，相比于其他方法，该方法可以在更好保持光谱信息的同时增强影像的空间分辨率，具有更广泛的适用范围和更佳的融合效果。

Maximum a Posteriori Fusion Method Based on Gradient Consistency Constraint for Multispectral/Panchromatic Remote Sensing Images

Multispectral (MS) images with high spatial resolution (HR) can be obtained by fusing MS images and panchromatic (PAN) image, the HR MS images have an important significance in image interpretation and classification, etc. In the present paper, a new image fusion method based on gradient consistency constraint for MS/PAN images is developed. The method is based on maximum a posteriori (MAP) framework. The relationship of desired HR MS images and PAN image is formulated by gradient consistency constraint. Observation model of MS images and the Huber-Markov priori are combined to solve the fused image by gradient descent algorithm. In the proposed method, gradient consistency constraint is introduced, and defect of band number restriction is overcomed in conventional model-based fusion methods. Iterative step for every band is solved adaptively, and spectral characteristics of each band are fully taken into account, so it not only ensures the spectral information fidelity, but also improves the integration degree of spatial information of fused image. The proposed method has been tested using IKONOS and WorldView-2 images. It is compared with GS, AIHS and AMBF fusion methods from both qualitative and quantitative aspects. Experimental results show that the proposed method can better preserve spectral information while enhance spatial resolution, and it has broader applicability and better fusion result than other methods.