In this paper, we introduce a very deep network with dense blocks and residual learning to tackle these problems.
A very deep network with dense blocks and residual learning to tackle the problems of small-scale data and gradient vanishing, which has been preventing ...
Jun 22, 2024 · In recent years, convolutional neural networks (CNNs) have shown promising performance in the field of multispectral (MS) and panchromatic (PAN) ...
Going Deeper with Densely Connected Convolutional Neural Networks for Multispectral Pansharpening □. Dong Wang, Ying Li, Jonathan C-W Chan.
Wang, D, Li, Y & Chan, JC-W 2019, ' Going Deeper with Densely Connected Convolutional Neural Networks for Multispectral Pansharpening ', Remote Sensing ...
Going deeper with densely connected convolutional neural networks for multispectral pansharpening. D Wang, Y Li, L Ma, Z Bai, JCW Chan. Remote Sensing 11 (22) ...
Jul 1, 2023 · A novel single-branch, single-scale lightweight convolutional neural network, named SDRCNN, is developed in this study. By using a novel dense ...
Abstract—In recent years, there has been a growing interest in deep learning-based pansharpening. Thus far, research has mainly focused on architectures.
This paper proposes a pansharpening method of MS images via multi-scale deep residual network (MSDRN).
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Nov 16, 2021 · The framework is fully general, and can be used to train and fine-tune any deep learning-based pansharpening network.