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Pan-sharpening is a process of merging a high-resolution (HR) panchromatic(PAN) image and its corresponding low-resolution (LR) multi-spectral (MS) imageto create an HR-MS and pan-sharpened image. However, due to the differentsensors' locations, characteristics and acquisition time, PAN and MS imagepairs often tend to have various amounts of misalignment. Conventionaldeep-learning-based methods that were trained with such misaligned PAN-MS imagepairs suffer from diverse artifacts such as double-edge and blur artifacts inthe resultant PAN-sharpened images. In this paper, we propose a novel frameworkcalled shift-invariant pan-sharpening with moving object alignment (SIPSA-Net)which is the first method to take into account such large misalignment ofmoving object regions for PAN sharpening. The SISPA-Net has a feature alignmentmodule (FAM) that can adjust one feature to be aligned to another feature, evenbetween the two different PAN and MS domains. For better alignment inpan-sharpened images, a shift-invariant spectral loss is newly designed, whichignores the inherent misalignment in the original MS input, thereby having thesame effect as optimizing the spectral loss with a well-aligned MS image.Extensive experimental results show that our SIPSA-Net can generatepan-sharpened images with remarkable improvements in terms of visual qualityand alignment, compared to the state-of-the-art methods.

SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery