Target-Adaptive CNN-Based Pansharpening

By Giuseppe Scarpa, Sergio Vitale, Davide Cozzolino. IEEE Transactions on Geoscience and Remote Sensing, 2017

350 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/05b313444c01dd76879da5b584f9d1a92d6fedf3

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Citations in the corpus

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, methodology): “Pansharpening Supervised A-PNN (Scarpa et al., 2018) Theano https://github.”
BockNet: Blind-Block Reconstruction Network With a Guard Window for Hyperspectral Anomaly Detection (2023, methodology): “We did not adopt the typical ℓ 2 loss given that it would be more concerned with larger errors but less sensitive to small ones [70].”
Super-Resolution of Sentinel-2 Images: Learning a Globally Applicable Deep Neural Network (2018, background): “More recently, this concept has been further exploited in Scarpa et al. (2018).”