Pansharpening via Detail Injection Based Convolutional Neural Networks
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TL;DR: This paper designs a new detail injection based convolutional neural network (DiCNN) framework for pansharpening with the MS details being directly formulated in end-to-end manners, where the first detail injections based CNN mines MS details through the PAN image and the MS image, and the second one utilizes only the PAN picture.
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Abstract: Pansharpening aims to fuse a multispectral (MS) image with an associated panchromatic (PAN) image, producing a composite image with the spectral resolution of the former and the spatial resolution of the latter. Traditional pansharpening methods can be ascribed to a unified detail injection context, which views the injected MS details as the integration of PAN details and bandwise injection gains. In this paper, we design a new detail injection based convolutional neural network (DiCNN) framework for pansharpening with the MS details being directly formulated in end-to-end manners, where the first detail injection based CNN (DiCNN1) mines MS details through the PAN image and the MS image, and the second one (DiCNN2) utilizes only the PAN image. The main advantage of the proposed DiCNNs is that they provide explicit physical interpretations and can achieve fast convergence while achieving high pansharpening quality. Furthermore, the effectiveness of the proposed approaches is also analyzed from a relatively theoretical point of view. Our methods are evaluated via experiments on real MS image datasets, achieving excellent performance when compared to other state-of-the-art methods.
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Citations
Unsupervised Deep Learning-Based Pansharpening With Jointly Enhanced Spectral and Spatial Fidelity
TL;DR: This work proposes a new DL-based pansharpening model that fully exploits the potential of this approach and provides cutting-edge performance, and features a novel loss function that jointly promotes the spectral and spatial quality of the panshARPened data.
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Two Stages Pan-Sharpening Details Injection Approach Based on Very Deep Residual Networks
TL;DR: In this article, a two-stage detail injection approach is proposed to reconstruct fine structures based on convolutional neural networks (CNNs) for pan-sharpening, which can achieve higher performance in both spatial and spectral qualities compared to the state of the art as well as the new CNN-based methods.
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Deep Learning in Diverse Intelligent Sensor Based Systems
TL;DR: Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering as mentioned in this paper , and the increasing complexity and large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted.
CADUI: Cross-Attention-Based Depth Unfolding Iteration Network for Pansharpening Remote Sensing Images
TL;DR: Wang et al. as mentioned in this paper proposed a cross-attention-based depth unfolding iteration network for pan-sharpening remote sensing images, which achieves the desired fusion effect by iteratively optimizing the deep prior regularization and combining it with a crossattention mechanism.
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SC-PNN: Saliency Cascade Convolutional Neural Network for Pansharpening
TL;DR: A new saliency cascade convolutional neural network for pansharpening (SC-PNN) that has a superior ability to improve the spatial quality and preserve spectral information and is validated in the experiment.
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