In recent years, there has been a growing interest in deep learning-based pansharpening. Thus far, research has mainly focused on architectures. Nonetheless, model training is an equally important issue. A first problem is the absence of ground truths, unavoidable in pansharpening. This is often addressed by training networks in a reduced resolution domain and using the original data as ground truth, relying on an implicit scale invariance assumption. However, on full resolution images results are often disappointing, suggesting such invariance not to hold. A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training test images. In this paper, we propose a full-resolution training framework for deep learning-based pansharpening. The framework is fully general and can be used for any deep learning-based pansharpening model. Training takes place in the high-resolution domain, relying only on the original data, thus avoiding any loss of information. To ensure spectral and spatial fidelity, a suitable two-component loss is defined. The spectral component enforces consistency between the pansharpened output and the low-resolution multispectral input. The spatial component, computed at high-resolution, maximizes the local correlation between each pansharpened band and the panchromatic input. At testing time, the target-adaptive operating modality is adopted, achieving good generalization with a limited computational overhead. Experiments carried out on WorldView-3, WorldView-2, and GeoEye-1 images show that methods trained with the proposed framework guarantee a pretty good performance in terms of both full-resolution numerical indexes and visual quality.
Pansharpening aims to fuse panchromatic and multispectral images from the satellite to generate images with both high spatial and spectral resolution. With the successful applications of deep learning in the computer vision field, a lot of scholars have proposed many convolutional neural networks (CNNs) to solve the pansharpening task. These pansharpening networks focused on various distinctive structures of CNNs, and most of them are trained by L2 loss between fused images and simulated desired multispectral images. However, L2 loss is designed to directly minimize the difference of spectral …
Pansharpening is a fundamental issue in remote sensing field. This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for pansharpening. The key idea is to split the low resolution multispectral image into a panchromatic image related feature map and a panchromatic image irrelated feature map, where the former one is regularized by the side information from panchromatic images. With the principle of algorithm unrolling techniques, the proposed model is generalized as a deep neural network, called as SCSC pansharpening neural network (SCSC-PNN). Compared…
Xi’an Jiaotong University（西安交通大学）；Shaanxi Normal University（陕西师范大学）
Recently, deep learning techniques have been extensively studied for pansharpening, which aims to generate a high resolution multispectral (HRMS) image by fusing a low resolution multispectral (LRMS) image with a high resolution panchromatic (PAN) image. However, existing deep learning-based pansharpening methods directly learn the mapping from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology…
A new model-based image adjustment for the enhancement of multi-resolution image fusion or pansharpening is proposed. Such image adjustment is needed for most pansharpening methods using panchromatic band and/or intensity image (calculated as a weighted sum of multispectral bands) as an input. Due various reasons, e.g. calibration inaccuracies, usage of different sensors, input images for pansharpening: low resolution multispectral image or more precisely the calculated intensity image and high resolution panchromatic image may differ in values of their physical properties, e.g. radiances or r…
Pansharpening is a widely used image enhancement technique for remote sensing. Its principle is to fuse the input high-resolution single-channel panchromatic (PAN) image and low-resolution multi-spectral image and to obtain a high-resolution multi-spectral (HRMS) image. The existing deep learning pansharpening method has two shortcomings. First, features of two input images need to be concatenated along the channel dimension to reconstruct the HRMS image, which makes the importance of PAN images not prominent, and also leads to high computational cost. Second, the implicit information of featu…
Pansharpening aims to fuse a registered high-resolution panchromatic image (PAN) with a low-resolution hyperspectral image (LR-HSI) to generate an enhanced HSI with high spectral and spatial resolution. Existing pansharpening approaches neglect using an attention mechanism to transfer HR texture features from PAN to LR-HSI features, resulting in spatial and spectral distortions. In this paper, we present a novel attention mechanism for pansharpening called HyperTransformer, in which features of LR-HSI and PAN are formulated as queries and keys in a transformer, respectively. HyperTransformer c…
COMSATS University Islamabad（伊斯兰堡COMSATS大学）；BUITEMS（俾路支省信息技术、工程与管理科学大学）；Bahria University Islamabad（伊斯兰堡巴赫里亚大学）
Retaining spatial characteristics of panchromatic image and spectral information of multispectral bands is a critical issue in pansharpening. This paper proposes a pyramid based deep fusion framework that preserves spectral and spatial characteristics at different scales. The spectral information is preserved by passing the corresponding low resolution multispectral image as residual component of the network at each scale. The spatial information is preserved by training the network at each scale with the high frequencies of panchromatic image alongside the corresponding low resolution multisp…
Remote Sensing Technology Institute, German Aerospace Center (DLR)（德国航空航天中心遥感技术研究所）
Validation of the quality of pansharpening methods is a difficult task because the reference is not directly available. In the meantime, two main approaches have been established: validation in reduced resolution and original resolution. In the former approach it is still not clear how the data are to be processed to a lower resolution. Other open issues are related to the question which resolution and measures should be used. In the latter approach the main problem is how the appropriate measure should be selected. In the most comparison studies the results of both approaches do not correspon…
Pansharpening in remote sensing image aims at acquiring a high-resolution multispectral (HRMS) image directly by fusing a low-resolution multispectral (LRMS) image with a panchromatic (PAN) image. The main concern is how to effectively combine the rich spectral information of LRMS image with the abundant spatial information of PAN image. Recently, many methods based on deep learning have been proposed for the pansharpening task. However, these methods usually has two main drawbacks: 1) requiring HRMS for supervised learning; and 2) simply ignoring the latent relation between the MS and PAN ima…
National University of Sciences and Technology（国家科技大学）；Technical University of Munich（慕尼黑工业大学）
Pansharpening enhances spatial details of high spectral resolution multispectral images using features of high spatial resolution panchromatic image. There are a number of traditional pansharpening approaches but producing an image exhibiting high spectral and spatial fidelity is still an open problem. Recently, deep learning has been used to produce promising pansharpened images; however, most of these approaches apply similar treatment to both multispectral and panchromatic images by using the same network for feature extraction. In this work, we present present a novel dual attention-based …
CrossDiff: Exploring Self-Supervised Representation of Pansharpening via Cross-Predictive Diffusion Model
Northwestern Polytechnical University（西北工业大学）；Research Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Shandong Normal University（山东师范大学）
In remote sensing, images acquired by various earth observation satellites tend to have either a high spatial and low spectral resolution or vice versa. Pansharpening is a technique which aims to improve spatial resolution of multispectral image. The challenges involve in the pansharpening are not only to improve the spatial resolution but also to preserve spectral quality of the multispectral image. In this paper, various pansharpening algorithms are discussed and classified based on approaches they have adopted. Using MATLAB image processing toolbox, several state-of-art pan-sharpening algor…
Istanbul Technical University (ITU)（伊斯坦布尔理工大学）；ITU-AI: Artificial Intelligence and Data Science Research and Application Center（伊斯坦布尔理工大学人工智能与数据科学研究与应用中心）；ITU - CSCRS (Application and Research Center for Satellite Communications and Remote Sensing)（伊斯坦布尔理工大学卫星通信与遥感应用研究中心）
Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years. However, they still exhibit limitations in producing high-quality pansharpening outputs. To that end, we propose a new self-supervised learning framework, where we treat pansharpening as a colorization problem, which brings an entirely novel perspective and solution to the problem compared to existing methods that base their solution solely on producing a super-resolution version of the multispectral image. Whereas CNN-based methods provide a reduced resolutio…
University Federico II（费德里科二世大学）；University Parthenope（帕台农佩大学）
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 which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as …
Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation
University Federico II（费德里科二世大学）；National Research Council, Institute of Methodologies for Environmental Analysis, CNR-IMAA（国家研究委员会环境分析方法研究所）；National Biodiversity Future Center（国家生物多样性未来中心）；University Parthenope（帕尔特诺珀大学）
University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）；Applied Research LLC（应用研究有限责任公司）
Pansharpening is to fuse a multispectral image (MSI) of low-spatial-resolution (LR) but rich spectral characteristics with a panchromatic image (PAN) of high-spatial-resolution (HR) but poor spectral characteristics. Traditional methods usually inject the extracted high-frequency details from PAN into the up-sampled MSI. Recent deep learning endeavors are mostly supervised assuming the HR MSI is available, which is unrealistic especially for satellite images. Nonetheless, these methods could not fully exploit the rich spectral characteristics in the MSI. Due to the wide existence of mixed pixe…
Cross-Scale Pansharpening via ScaleFormer and the PanScale Benchmark
HFIPS, Chinese Academy of Sciences（中国科学院HFIPS）；University of Science and Technology of China（中国科学技术大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；The University of Hong Kong（香港大学）；Xiamen University（厦门大学）；Zhejiang University（浙江大学）
Most satellites decouple the acquisition of a panchromatic image at high spatial resolution from the acquisition of a multispectral image at lower spatial resolution. Pansharpening is a fusion technique used to increase the spatial resolution of the multispectral data while simultaneously preserving its spectral information. In this paper, we consider pansharpening as an optimization problem minimizing a cost function with a nonlocal regularization term. The energy functional which is to be minimized decouples for each band, thus permitting the application to misregistered spectral components.…
GSPan: A Continuous Gaussian Primitive Representation for Arbitrary-Scale Pansharpening
Beihang University（北京航空航天大学）；Tsinghua University（清华大学）；National Research Council - Institute of Methodologies for Environmental Analysis, CNR-IMAA（意大利国家研究委员会 - 环境分析方法研究所）
Rice University（莱斯大学）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
Blind pansharpening addresses the problem of generating a high spatial-resolution multi-spectral (HRMS) image given a low spatial-resolution multi-spectral (LRMS) image with the guidance of its associated spatially misaligned high spatial-resolution panchromatic (PAN) image without parametric side information. In this paper, we propose a fast approach to blind pansharpening and achieve state-of-the-art image reconstruction quality. Typical blind pansharpening algorithms are often computationally intensive since the blur kernel and the target HRMS image are often computed using iterative solver…
Yingcai Honors College（英才荣誉学院）；University of Electronic Science and Technology of China（电子科技大学）；School of Mathematical Sciences（数学科学学院）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；LJK（让·昆兹实验室）；National Research Council - Institute of Methodologies for Environmental Analysis（国家研究委员会 - 环境分析方法研究所）；CNR-IMAA（国家研究委员会 - 环境分析方法研究所）
Pansharpening refers to the fusion of a panchromatic image with a high spatial resolution and a multispectral image with a low spatial resolution, aiming to obtain a high spatial resolution multispectral image. In this paper, we propose a novel deep neural network architecture with level-domain based loss function for pansharpening by taking into account the following double-type structures, \emph{i.e.,} double-level, double-branch, and double-direction, called as triple-double network (TDNet). By using the structure of TDNet, the spatial details of the panchromatic image can be fully exploite…
Wavelet-Assisted Multi-Frequency Attention Network for Pansharpening
University of Electronic Science and Technology of China（电子科技大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Fast Model-guided Instance-wise Adaptation Framework for Real-world Pansharpening with Fidelity Constraints
University of Electronic Science and Technology of China（电子科技大学）；National Research Council, Institute of Methodologies for Environmental Analysis (CNR-IMAA)（国家研究委员会，环境分析方法研究所（CNR-IMAA））
Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives
University Federico II（那不勒斯费德里科二世大学）；National Research Council（国家研究委员会）；National Biodiversity Future Center（国家生物多样性未来中心）；INRIA（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；University of Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Escuela Politecnica de Caceres（卡塞雷斯理工学院）；University Parthenope（帕尔特诺佩大学）
Gipsa-lab（Gipsa实验室）；ONERA（法国航空航天实验室）；Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）；University of Toulouse（图卢兹大学）；Ghent University（根特大学）；North Atlantic Treaty Organization (NATO) Science and Technology Organization (STO) Centre for Maritime Research and Experimentation (CMRE)（北大西洋公约组织（NATO）科学与技术组织（STO）海事研究与实验中心（CMRE））；University of Tokyo（东京大学）
Pansharpening aims at fusing a panchromatic image with a multispectral one, to generate an image with the high spatial resolution of the former and the high spectral resolution of the latter. In the last decade, many algorithms have been presented in the literature for pansharpening using multispectral data. With the increasing availability of hyperspectral systems, these methods are now being adapted to hyperspectral images. In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which hav…
CMT: Cross Modulation Transformer with Hybrid Loss for Pansharpening
School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China（电子科技大学光电科学与工程学院）；School of Science, Xihua University（西华大学理学院）；School of Mathematical Sciences, University of Electronic Science and Technology of China（电子科技大学数学科学学院）
Dual Modality Prompted Diffusion Priors for Zero Shot Hyperspectral Pansharpening
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Peking University（北京大学）；National Center for Applied Mathematics Shenzhen (NCAMS), Southern University of Science and Technology（南方科技大学深圳应用数学国家中心）；Department of Automation, Tsinghua University（清华大学自动化系）；National Research Council of Italy, Institute of Integrated Methodologies for Earth Observation (CNR-IMIOT)（意大利国家研究委员会综合地球观测方法研究所）
Whiting School of Engineering（怀廷工程学院）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Hyperspectral pansharpening aims to synthesize a low-resolution hyperspectral image (LR-HSI) with a registered panchromatic image (PAN) to generate an enhanced HSI with high spectral and spatial resolution. Recently proposed HS pansharpening methods have obtained remarkable results using deep convolutional networks (ConvNets), which typically consist of three steps: (1) up-sampling the LR-HSI, (2) predicting the residual image via a ConvNet, and (3) obtaining the final fused HSI by adding the outputs from first and second steps. Recent methods have leveraged Deep Image Prior (DIP) to up-sample…
Recently, convolutional neural networks (CNN) have obtained promising results in single-image SR for hyperspectral pansharpening. However, enhancing CNNs' representation ability with fewer parameters and a shorter prediction time is a challenging and critical task. In this paper, we propose a novel multi-spectral image fusion method using a combination of the previously proposed 3D CNN model VolumeNet and 2.5D texture transfer method using other modality high resolution (HR) images. Since a multi-spectral (MS) image consists of several bands and each band is a 2D image slice, MS images can be …
School of Electronic Information, Wuhan University（武汉大学电子信息学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Zhongguancun Academy（中关村学院）
CGFformer: Cluster-Guidance Frequency Transformer for Pansharpening
College of Artificial Intelligence, Xi’an Jiaotong University（西安交通大学人工智能学院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学学院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）
Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods used in these approaches only utilize the local information of each pixel in the low-resolution multispectral (LRMS) image while neglecting to exploit its global information as well as the cross-modal information of the guiding panchromatic (PAN) image, which limits their performance improvement. To address this issue, this paper develops a novel probability-based global cross-modal upsampling (PGCU) method for pan-…
Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control
Department of Electrical Engineering and Information Technology, University Federico II（费米二世大学电气工程与信息科技系）；National Research Council, Institute of Methodologies for Environmental Analysis（国家研究 council 环境分析方法研究所）；Department of Engineering, University Parthenope（帕尔泰诺大学工程系）
Euler-inspired Decoupling Neural Operator for Efficient Pansharpening
School of Software Technology, Zhejiang University（浙江大学软件技术学院）；School of Computer Science and Technology , Chongqing University of Post and Telecommunications（重庆邮电大学计算机科学与技术学院）
