Pansharpening via Detail Injection Based Convolutional Neural Networks
221
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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Laplacian pyramid networks: A new approach for multispectral pansharpening
01 Feb 2022
TL;DR: Cheng et al. as discussed by the authors proposed a Laplacian pyramid pansharpening network architecture for accurately fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image, aiming at getting a higher spatial resolution multi-spectral image.
26
Dual-Collaborative Fusion Model for Multispectral and Panchromatic Image Fusion
TL;DR: Wang et al. as mentioned in this paper proposed a dual-collaborative fusion model that considers not only the spectral correlation collaboration but also the spatial-spectral collaboration, and a novel graph convolutional network is designed for the modulation of intraspectral relationships.
25
SpanConv: A New Convolution via Spanning Kernel Space for Lightweight Pansharpening
Zhi-Xuan Chen,Cheng Jin,Tian-Jing Zhang,Xiao Wu,Liang-Jian Deng +4 more
- 01 Jul 2022
TL;DR: This paper focuses on kernel generation and presents an interpretable span strategy, named SpanConv, for the effective construction of kernel space, which first learn two navigated kernels with single channel as bases, then extend the two kernels by learnable coefficients, and finally span the two sets of kernels by their linear combination to construct the so-called SpanKernel.
Source-Adaptive Discriminative Kernels based Network for Remote Sensing Pansharpening
Siran Peng,Liang-Jian Deng,Jin-Fan Hu,Yu-Wei Zhuo +3 more
- 01 Jul 2022
TL;DR: ADKNet as discussed by the authors proposes a convolutional neural network with source-adaptive discriminative kernels, which consist of spatial kernels generated from PAN images containing rich spatial details and spectral kernels from LR-MS images containing abundant spectral information.
Band-Independent Encoder–Decoder Network for Pan-Sharpening of Remote Sensing Images
TL;DR: The proposed band-independent encoder–decoder network outperforms several state-of-the-art pan-sharpening methods in both visual appearance and objective indexes, and the single-band evaluation results further verify the superiority of the proposed method.
23
References
Deep Residual Learning for Image Recognition
Kaiming He,Xiangyu Zhang,Shaoqing Ren,Jian Sun +3 more
- 27 Jun 2016
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
•Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
- 01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
138.5K
•Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
117.9K
•Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
82.5K
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
- 01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
53.5K