Differential Information Residual Convolutional Neural Network for Pansharpening

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In this paper, a new pansharpening method with residual convolutional neural network (RCNN)) is proposed. The proposed method utilizes a novel end-to-end CNN, which maps the differential information between the high spatial resolution panchromatic image (HR-PAN) and the low spatial resolution multispectral image (LR-MS) to the differential information between the HR-PAN image and the high spatial resolution multispectral image (HR-MS). Unlike the CNN-based pansharpening methods in other literatures, the proposed method makes full use of the spatial information in the HR-PAN image, and simultaneously preserve the spectral information of the MS image. Experimental results at both reduced resolution and full resolution demonstrate the superior performance of the proposed method comparing to state-of-the-art pansharpening methods in both quantitative and visual assessments.