DCPNet: A Dual-Task Collaborative Promotion Network for Pansharpening

Pansharpening, a type of image fusion, combines a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LRMS) image to produce a high-resolution multispectral (HRMS) output. Recent advancements in deep learning have led to the development of data-driven pansharpening methods, which have shown superior performance over traditional nondata-driven approaches. Nevertheless, current data-driven methods primarily focus on the design of single-task-driven networks. They tend to ignore the positive impact that incorporating auxiliary tasks could have on pansharpening. This oversight restricts further improvements in their performance. To break the constraints of single-task methods, we propose a dual-task collaborative promotion network (DCPNet) for pansharpening. DCPNet incorporates an LRMS image super-resolution (LRMS-SR) reconstruction network into the pansharpening network, establishing a dual-task parallel collaborative framework that achieves joint collaborative optimization for both pansharpening and SR reconstruction. In addition, we devise a feature fusion scheme that jointly reinforces spectral and spatial details, establishing a bridge for information interaction between the two task branches. This enables the two branches to extract higher quality features in a collaborative way, thus facilitating the realization of the interaction and fusion of dual-task features, as well as the exploration of potential information within the fused features. Experimental results demonstrate that our approach not only effectively transfers spectral information from the LRMS images to the fusion result but also preserves spatial details from the PAN images. The code is available at https://github.com/lhf12278/DCPNet.

• Publication year

  2024

• Venue

  IEEE Transactions on Geoscience and Remote Sensing

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Environmental Science

• Identifiers
  DOI 10.1109/TGRS.2024.3377635
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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