Philosophy

Cartography

Real-time computing

Set (abstract data type)

Computer science

Geology

Oceanography

Software

Pattern recognition (psychology)

Geography

Epistemology

Artificial intelligence

Train

Programming language

Convolutional neural network

Property (philosophy)

Baseline (sea)

Computer vision

IEEE Transactions on Geoscience and Remote Sensing

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network that trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality that ensures a very good performance also in the presence of a mismatch with respect to the training set and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware.

Target-Adaptive CNN-Based Pansharpening