Relevant bibliographies by topics / Multispectral pansharpening

Academic literature on the topic 'Multispectral pansharpening'

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Published: 10 March 2023

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Journal articles on the topic "Multispectral pansharpening"

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Choi, Jaewan, Honglyun Park, and Doochun Seo. "Pansharpening Using Guided Filtering to Improve the Spatial Clarity of VHR Satellite Imagery." Remote Sensing 11, no. 6 (March 15, 2019): 633. http://dx.doi.org/10.3390/rs11060633.

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Pansharpening algorithms are designed to enhance the spatial resolution of multispectral images using panchromatic images with high spatial resolutions. Panchromatic and multispectral images acquired from very high resolution (VHR) satellite sensors used as input data in the pansharpening process are characterized by spatial dissimilarities due to differences in their spectral/spatial characteristics and time lags between panchromatic and multispectral sensors. In this manuscript, a new pansharpening framework is proposed to improve the spatial clarity of VHR satellite imagery. This algorithm aims to remove the spatial dissimilarity between panchromatic and multispectral images using guided filtering (GF) and to generate the optimal local injection gains for pansharpening. First, we generate optimal multispectral images with spatial characteristics similar to those of panchromatic images using GF. Then, multiresolution analysis (MRA)-based pansharpening is applied using normalized difference vegetation index (NDVI)-based optimal injection gains and spatial details obtained through GF. The algorithm is applied to Korea multipurpose satellite (KOMPSAT)-3/3A satellite sensor data, and the experimental results show that the pansharpened images obtained with the proposed algorithm exhibit a superior spatial quality and preserve spectral information better than those based on existing algorithms.
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Liu, Junmin, Jing Ma, Rongrong Fei, Huirong Li, and Jiangshe Zhang. "Enhanced Back-Projection as Postprocessing for Pansharpening." Remote Sensing 11, no. 6 (March 25, 2019): 712. http://dx.doi.org/10.3390/rs11060712.

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Pansharpening is the process of integrating a high spatial resolution panchromatic image with a low spatial resolution multispectral image to obtain a multispectral image with high spatial and spectral resolution. Over the last decade, several algorithms have been developed for pansharpening. In this paper, a technique, called enhanced back-projection (EBP), is introduced and applied as postprocessing on the pansharpening. The proposed EBP first enhances the spatial details of the pansharpening results by histogram matching and high-pass modulation, followed by a back-projection process, which takes into account the modulation transfer function (MTF) of the satellite sensor such that the pansharpening results obey the consistency property. The EBP is validated on four datasets acquired by different satellites and several commonly used pansharpening methods. The pansharpening results achieve substantial improvements by this postprocessing technique, which is widely applicable and requires no modification of existing pansharpening methods.
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Wang, Wenqing, Zhiqiang Zhou, Xiaoqiao Zhang, Tu Lv, Han Liu, and Lili Liang. "DiTBN: Detail Injection-Based Two-Branch Network for Pansharpening of Remote Sensing Images." Remote Sensing 14, no. 23 (December 2, 2022): 6120. http://dx.doi.org/10.3390/rs14236120.

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Pansharpening is one of the main research topics in the field of remote sensing image processing. In pansharpening, the spectral information from a low spatial resolution multispectral (LRMS) image and the spatial information from a high spatial resolution panchromatic (PAN) image are integrated to obtain a high spatial resolution multispectral (HRMS) image. As a prerequisite for the application of LRMS and PAN images, pansharpening has received extensive attention from researchers, and many pansharpening methods based on convolutional neural networks (CNN) have been proposed. However, most CNN-based methods regard pansharpening as a super-resolution reconstruction problem, which may not make full use of the feature information in two types of source images. Inspired by the PanNet model, this paper proposes a detail injection-based two-branch network (DiTBN) for pansharpening. In order to obtain the most abundant spatial detail features, a two-branch network is designed to extract features from the high-frequency component of the PAN image and the multispectral image. Moreover, the feature information provided by source images is reused in the network to further improve information utilization. In order to avoid the training difficulty for a real dataset, a new loss function is introduced to enhance the spectral and spatial consistency between the fused HRMS image and the input images. Experiments on different datasets show that the proposed method achieves excellent performance in both qualitative and quantitative evaluations as compared with several advanced pansharpening methods.
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Pérez-Bueno, Fernando, Miguel Vega, Javier Mateos, Rafael Molina, and Aggelos K. Katsaggelos. "Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors." Sensors 20, no. 18 (September 16, 2020): 5308. http://dx.doi.org/10.3390/s20185308.

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Pansharpening is a technique that fuses a low spatial resolution multispectral image and a high spatial resolution panchromatic one to obtain a multispectral image with the spatial resolution of the latter while preserving the spectral information of the multispectral image. In this paper we propose a variational Bayesian methodology for pansharpening. The proposed methodology uses the sensor characteristics to model the observation process and Super-Gaussian sparse image priors on the expected characteristics of the pansharpened image. The pansharpened image, as well as all model and variational parameters, are estimated within the proposed methodology. Using real and synthetic data, the quality of the pansharpened images is assessed both visually and quantitatively and compared with other pansharpening methods. Theoretical and experimental results demonstrate the effectiveness, efficiency, and flexibility of the proposed formulation.
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He, Lin, Dahan Xi, Jun Li, and Jiawei Zhu. "A Spectral-Aware Convolutional Neural Network for Pansharpening." Applied Sciences 10, no. 17 (August 22, 2020): 5809. http://dx.doi.org/10.3390/app10175809.

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Pansharpening aims at fusing a low-resolution multiband optical (MBO) image, such as a multispectral or a hyperspectral image, with the associated high-resolution panchromatic (PAN) image to yield a high spatial resolution MBO image. Though having achieved superior performances to traditional methods, existing convolutional neural network (CNN)-based pansharpening approaches are still faced with two challenges: alleviating the phenomenon of spectral distortion and improving the interpretation abilities of pansharpening CNNs. In this work, we develop a novel spectral-aware pansharpening neural network (SA-PNN). On the one hand, SA-PNN employs a network structure composed of a detail branch and an approximation branch, which is consistent with the detail injection framework; on the other hand, SA-PNN strengthens processing along the spectral dimension by using a spectral-aware strategy, which involves spatial feature transforms (SFTs) coupling the approximation branch with the detail branch as well as 3D convolution operations in the approximation branch. Our method is evaluated with experiments on real-world multispectral and hyperspectral datasets, verifying its excellent pansharpening performance.
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Guo, Yecai, Fei Ye, and Hao Gong. "Learning an Efficient Convolution Neural Network for Pansharpening." Algorithms 12, no. 1 (January 8, 2019): 16. http://dx.doi.org/10.3390/a12010016.

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Pansharpening is a domain-specific task of satellite imagery processing, which aims at fusing a multispectral image with a corresponding panchromatic one to enhance the spatial resolution of multispectral image. Most existing traditional methods fuse multispectral and panchromatic images in linear manners, which greatly restrict the fusion accuracy. In this paper, we propose a highly efficient inference network to cope with pansharpening, which breaks the linear limitation of traditional methods. In the network, we adopt a dilated multilevel block coupled with a skip connection to perform local and overall compensation. By using dilated multilevel block, the proposed model can make full use of the extracted features and enlarge the receptive field without introducing extra computational burden. Experiment results reveal that our network tends to induce competitive even superior pansharpening performance compared with deeper models. As our network is shallow and trained with several techniques to prevent overfitting, our model is robust to the inconsistencies across different satellites.
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Yang, Yong, Wei Tu, Shuying Huang, and Hangyuan Lu. "PCDRN: Progressive Cascade Deep Residual Network for Pansharpening." Remote Sensing 12, no. 4 (February 19, 2020): 676. http://dx.doi.org/10.3390/rs12040676.

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Pansharpening is the process of fusing a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image. In the process of pansharpening, the LRMS image is often directly upsampled by a scale of 4, which may result in the loss of high-frequency details in the fused high-resolution multispectral (HRMS) image. To solve this problem, we put forward a novel progressive cascade deep residual network (PCDRN) with two residual subnetworks for pansharpening. The network adjusts the size of an MS image to the size of a PAN image twice and gradually fuses the LRMS image with the PAN image in a coarse-to-fine manner. To prevent an overly-smooth phenomenon and achieve high-quality fusion results, a multitask loss function is defined to train our network. Furthermore, to eliminate checkerboard artifacts in the fusion results, we employ a resize-convolution approach instead of transposed convolution for upsampling LRMS images. Experimental results on the Pléiades and WorldView-3 datasets prove that PCDRN exhibits superior performance compared to other popular pansharpening methods in terms of quantitative and visual assessments.
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Cao, Xiangyong, Yang Chen, and Wenfei Cao. "Proximal PanNet: A Model-Based Deep Network for Pansharpening." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 176–84. http://dx.doi.org/10.1609/aaai.v36i1.19892.

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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 with the deep learning method. Firstly, we build an observation model for pansharpening using the convolutional sparse coding (CSC) technique and design a proximal gradient algorithm to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Finally, all the learnable modules can be automatically learned in an end-to-end manner. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.
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Li, X. J., H. W. Yan, S. W. Yang, L. Kang, and X. M. Lu. "MULTISPECTRAL PANSHARPENING APPROACH USING PULSE-COUPLED NEURAL NETWORK SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 961–65. http://dx.doi.org/10.5194/isprs-archives-xlii-3-961-2018.

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The paper proposes a novel pansharpening method based on the pulse-coupled neural network segmentation. In the new method, uniform injection gains of each region are estimated through PCNN segmentation rather than through a simple square window. Since PCNN segmentation agrees with the human visual system, the proposed method shows better spectral consistency. Our experiments, which have been carried out for both suburban and urban datasets, demonstrate that the proposed method outperforms other methods in multispectral pansharpening.
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Li, Xiaojun, Haowen Yan, Weiying Xie, Lu Kang, and Yi Tian. "An Improved Pulse-Coupled Neural Network Model for Pansharpening." Sensors 20, no. 10 (May 12, 2020): 2764. http://dx.doi.org/10.3390/s20102764.

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Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.
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Dissertations / Theses on the topic "Multispectral pansharpening"

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Vivone, Gemine. "Multispectral and hyperspectral pansharpening." Doctoral thesis, Universita degli studi di Salerno, 2014. http://hdl.handle.net/10556/1604.

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2012-2013
Remote sensing consists in measuring some characteristics of an object from a distance. A key example of remote sensing is the Earth observation from sensors mounted on satellites that is a crucial aspect of space programs. The first satellite used for Earth observation was Explorer VII. It has been followed by thousands of satellites, many of which are still working. Due to the availability of a large number of different sensors and the subsequent huge amount of data collected, the idea of obtaining improved products by means of fusion algorithms is becoming more intriguing. Data fusion is often exploited for indicating the process of integrating multiple data and knowledge related to the same real-world scene into a consistent, accurate, and useful representation. This term is very generic and it includes different levels of fusion. This dissertation is focused on the low level data fusion, which consists in combining several sources of raw data. In this field, one of the most relevant scientific application is surely the Pansharpening. Pansharpening refers to the fusion of a panchromatic image (a single band that covers the visible and near infrared spectrum) and a multispectral/hyperspectral image (tens/hundreds bands) acquired on the same area. [edited by author]
XII ciclo n.s.
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Jacq, Kévin. "Traitement d'images multispectrales et spatialisation des données pour la caractérisation de la matière organique des phases solides naturelles. High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core High-resolution grain size distribution of sediment core with 2 hyperspectral imaging Study of pansharpening methods applied to hyperspectral images of sediment cores." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAA024.

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L'évolution de l'environnement et le climat sont, actuellement, au centre de toutes les attentions. Les impacts de l'activité des sociétés actuelles et passées sur l'environnement sont notamment questionnés pour mieux anticiper les implications de nos activités sur le futur. Mieux décrire les environnements passés et leurs évolutions sont possibles grâce à l'étude de nombreux enregistreurs naturels (sédiments, spéléothèmes, cernes, coraux). Grâce à eux, il est possible de caractériser des évolutions bio-physico-chimiques à différentes résolutions temporelles et pour différentes périodes. La haute résolution entendue ici comme la résolution su sante pour l'étude de l'environnement en lien avec l'évolution des sociétés constitue le principal verrou de l'étude de ces archives naturelles notamment en raison de la capacité analytique des appareils qui ne peuvent que rarement voir des structures fines inframillimétriques. Ce travail est bâti autour de l'hypothèse que l'utilisation de caméras hyperspectrales (VNIR, SWIR, LIF) couplée à des méthodes statistiques pertinentes doivent permettre d'accéder aux informations spectrales et donc bio-physico-chimiques contenues dans ces archives naturelles à une résolution spatiale de quelques dizaines de micromètres et, donc, de proposer des méthodes pour atteindre la haute résolution temporelle (saisonnière). De plus, a n d'avoir des estimations ables, plusieurs capteurs d'imageries et de spectroscopies linéaires (XRF, TRES) sont utilisés avec leurs propres caractéristiques (résolutions, gammes spectrales, interactions atomiques/moléculaires). Ces méthodes analytiques sont utilisées pour la caractérisation de la surface des carottes sédimentaires. Ces analyses spectrales micrométriques sont mises en correspondance avec des analyses géochimiques millimétriques usuelles. Optimiser la complémentarité de toutes ces données, implique de développer des méthodes permettant de dépasser la difficulté inhérente au couplage de données considérées par essence dissimilaire (résolutions, décalages spatiaux, non-recouvrement spectral). Ainsi, quatre méthodes ont été développées. La première consiste à associer les méthodes hyperspectrales et usuelles pour la création de modèles prédictifs quantitatifs. La seconde permet le recalage spatial des différentes images hyperspectrales à la plus basse des résolutions. La troisième s'intéresse à la fusion de ces dernières à la plus haute des résolutions. Enfin, la dernière s'intéresse aux dépôts présents dans les sédiments (lamines, crues, tephras) pour ajouter une dimension temporelle à nos études. Grâce à l'ensemble de ces informations et méthodes, des modèles prédictifs multivariés ont été estimés pour l'étude de la matière organique, des paramètres texturaux et de la distribution granulométrique. Les dépôts laminés et instantanés au sein des échantillons ont été caractérisés. Ceci a permis d'estimer des chroniques de crues, ainsi que des variations biophysico-chimiques à l'échelle de la saison. L'imagerie hyperspectrale couplée à des méthodes d'analyse des données sont donc des outils performants pour l'étude des archives naturelles à des résolutions temporelles fines. L'approfondissement des approches proposées dans ces travaux permettra d'étudier de multiples archives pour caractériser des évolutions à l'échelle d'un ou de plusieurs bassin(s) versant(s)
The evolution of the environment and climate are, currently, the focus of all attention. The impacts of the activities of present and past societies on the environment are in particular questioned in order to better anticipate the implications of our current activities on the future. Better describing past environments and their evolutions are possible thanks to the study of many natural recorders (sediments, speleothems, tree rings, corals). Thanks to them, it is possible to characterize biological-physical-chemical evolutions at di erent temporal resolutions and for di erent periods. The high resolution understood here as the su cient resolution for the study of the environment in connection with the evolution of societies constitutes the main lock of the study of these natural archives in particular because of the analytical capacity devices that can only rarely see ne inframillimetre structures. This work is built on the assumption that the use of hyperspectral sensors (VNIR, SWIR, LIF) coupled with relevant statistical methods should allow access to the spectral and therefore biological-physical-chemical contained in these natural archives at a spatial resolution of a few tens of micrometers and, therefore, to propose methods to reach the high temporal resolution (season). Besides, to obtain reliable estimates, several imaging sensors and linear spectroscopy (XRF, TRES) are used with their own characteristics (resolutions, spectral ranges, atomic/molecular interactions). These analytical methods are used for surface characterization of sediment cores. These micrometric spectral analyses are mapped to usual millimeter geochemical analyses. Optimizing the complementarity of all these data involves developing methods to overcome the di culty inherent in coupling data considered essentially dissimilar (resolutions, spatial shifts, spectral non-recovery). Thus, four methods were developed. The rst consists in combining hyperspectral and usual methods for the creation of quantitative predictive models. The second allows the spatial registration of di erent hyperspectral images at the lowest resolution. The third focuses on their merging with the highest of the resolutions. Finally, the last one focuses on deposits in sediments (laminae, oods, tephras) to add a temporal dimension to our studies. Through all this information and methods, multivariate predictive models were estimated for the study of organic matter, textural parameters and particle size distribution. The laminated and instantaneous deposits within the samples were characterized. These made it possible to estimate oods chronicles, as well as biological-physical-chemical variations at the season scale. Hyperspectral imaging coupled with data analysis methods are therefore powerful tools for the study of natural archives at ne temporal resolutions. The further development of the approaches proposed in this work will make it possible to study multiple archives to characterize evolutions at the scale of one or more watershed(s)
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Arienzo, Alberto. "Multi-sensor Model-based Data Fusion for Remote Sensing Applications." Doctoral thesis, 2022. http://hdl.handle.net/2158/1272763.

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The thesis addresses a widespread topic of remote sensing, namely pansharpening, representing a specific instance of image fusion, where a panchromatic image, characterized by high spatial resolution and no spectral information, is pixelwise merged with a set of multispectral images, featuring complementary characteristics, i.e., lower spatial resolution and spectral diversity. Thus, the aim of pansharpening is to generate a final image product featuring the spatial information of the panchromatic and the spectral content of the multispectral data. The first contribution of the thesis is to provide a twofold representation of the state of the art of pansharpening: one from a fusion methodology perspective and one from a quality assessment standpoint. Initially, we present a review of the most widespread fusion techniques and algorithms, with particular attention to the following major categories: Component Substitution, Multi-Resolution Analysis, Variational Optimization, and Machine Learning. Furthermore, several state-of-the-art hybrid approaches, involving any combinations of the former categories, are also described. Thereafter, we introduce a second review of the most popular quality evaluation protocols, both at full and reduced resolutions, proposed over the years in the corresponding literature. The second contribution of the thesis is to present an investigation on the data-format reproducibility of pansharpening, both in terms of fusion and quality assessment. The aim of this study is to demonstrate whether the pansharpening process is influenced by the particular data-format over which the input imagery is represented, such as digital number, spectral radiance and spectral reflectance. It will be theoretically proven and experimentally demonstrated that Multi-Resolution Analysis methods are unaffected by the format of the data, which is not always true for Component Substitution methods; for the latter, only the employment of regression-based solutions allows to reach data-format reproducibility. On the quality assessment, it will be demonstrated that purely spectral indexes, such as the Spectral Angle Mapper, feature a significant data-format dependence, whereas for indexes balancing the spectral and radiometric similarity, like those based on hypercomplex numbers, i.e., Q2n, such a dependence weakens and completely vanishes for purely radiometric indexes, such as those based on error summation, e.g., Relative Dimensionless Global Error in Synthesis. The third and final contribution of the thesis is to provide a critical comparison of the most widespread full-resolution quality assessment protocols, such as the quality-with-no-reference, QNR, and its more recent variations, a.k.a QNR-like. Specifically, we present a thorough discussion of the pros and cons of each protocol, aimed at identifying strengths and weaknesses in order to support future research developments. In addition, the problem of the combination of the two spatio-spectral distortion indexes forming the general QNR-like index, is also addressed, by studying and testing solutions based on coefficient estimation instead of exploiting coefficients that are fixed to a constant value. Experiments both at reduced and full resolutions, comprising a wide qualitative analysis, are considered to support the statements on the QNR-like protocols. The study highlights the interesting features of the Filter-based QNR protocol and the spatial distortion index of the Regression-based QNR, thus suggesting the use of these complementary quality assessment measures to provide a comprehensive and consistent assessment at full resolution.
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Book chapters on the topic "Multispectral pansharpening"

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Amro, Israa, and Javier Mateos. "Multispectral Image Pansharpening Based on the Contourlet Transform." In Information Optics and Photonics, 247–61. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7380-1_20.

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Liu, Bin, Weijie Liu, and Longxiang Xu. "Construction of Sampling Two-Channel Nonseparable Wavelet Filter Bank and Its Fusion Application for Multispectral Image Pansharpening." In Advances in Multimedia Information Processing – PCM 2017, 859–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77383-4_84.

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"Pansharpening of Multispectral Images." In Remote Sensing Image Fusion, 169–202. CRC Press, 2015. http://dx.doi.org/10.1201/b18189-14.

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Conference papers on the topic "Multispectral pansharpening"

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Aly, Hussein A., and Gaurav Sharma. "Joint multichannel pansharpening for multispectral imagery." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638036.

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Kaplan, N. H., and I. Erer. "Bilateral pyramid based pansharpening of multispectral satellite images." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6351017.

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Yao, Hong-Yu, Peng Wang, Xun Shen, Lixin Shi, and Chunlei Zhao. "Multispectral Pansharpening Based on High-Pass Modulation Regression." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883745.

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Akoguz, Alper, Burak Kurt, and Sedef K. Pinar. "Pansharpening of multispectral images using filtering in Fourier domain." In SPIE Remote Sensing, edited by Lorenzo Bruzzone, Jon Atli Benediktsson, and Francesca Bovolo. SPIE, 2014. http://dx.doi.org/10.1117/12.2067255.

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Xiao, Shi-Shi, Cheng Jin, Tian-Jing Zhang, Ran Ran, and Liang-Jian Deng. "Progressive Band-Separated Convolutional Neural Network for Multispectral Pansharpening." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9554024.

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Tarverdiyev, Vazirkhan, Isin Erer, Nur Huseyin Kaplan, and Nebiye Musaoglu. "Target Detection in Multispectral Images via Detail Enhanced Pansharpening." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884355.

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Peng, Siran, Liang-Jian Deng, Jin-Fan Hu, and Yuwei Zhuo. "Source-Adaptive Discriminative Kernels based Network for Remote Sensing Pansharpening." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/179.

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For the pansharpening problem, previous convolutional neural networks (CNNs) mainly concatenate high-resolution panchromatic (PAN) images and low-resolution multispectral (LR-MS) images in their architectures, which ignores the distinctive attributes of different sources. In this paper, we propose a convolution network with source-adaptive discriminative kernels, called ADKNet, for the pansharpening task. Those kernels consist of spatial kernels generated from PAN images containing rich spatial details and spectral kernels generated from LR-MS images containing abundant spectral information. The kernel generating process is specially designed to extract information discriminately and effectively. Furthermore, the kernels are learned in a pixel-by-pixel manner to characterize different information in distinct areas. Extensive experimental results indicate that ADKNet outperforms current state-of-the-art (SOTA) pansharpening methods in both quantitative and qualitative assessments, in the meanwhile only with about 60,000 network parameters. Also, the proposed network is extended to the hyperspectral image super-resolution (HSISR) problem, still yields SOTA performance, proving the universality of our model. The code is available at http://github.com/liangjiandeng/ADKNet.
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Bendoumi, Mohamed Amine, Tarek Benlefki, and Riad Saadi. "Pansharpening Multispectral Images Based on Unconstrained Least Square Spectral Unmixing." In 2018 International Conference on Signal, Image, Vision and their Applications (SIVA ). IEEE, 2018. http://dx.doi.org/10.1109/siva.2018.8660988.

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Qu, Ying, Hairong Qi, Bulent Ayhan, Chiman Kwan, and Richard Kidd. "DOES multispectral / hyperspectral pansharpening improve the performance of anomaly detection?" In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8128408.

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Aiazzi, Bruno, Luciano Alparone, Alberto Arienzo, Andrea Garzelli, and Simone Lolli. "Fast multispectral pansharpening based on a hyper-ellipsoidal color space." In Image and Signal Processing for Remote Sensing XXV, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2019. http://dx.doi.org/10.1117/12.2533481.

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You might also be interested in the extended bibliographies on the topic 'Multispectral pansharpening' for particular source types:
Journal articles
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