Relevant bibliographies by topics / Hyperspectral and multispectral data fusion

Contents

  1. Journal articles

Academic literature on the topic 'Hyperspectral and multispectral data fusion'

Author: Grafiati
Published: 6 September 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Hyperspectral and multispectral data fusion.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Hyperspectral and multispectral data fusion"

1

Chakravortty, S., and P. Subramaniam. "Fusion of Hyperspectral and Multispectral Image Data for Enhancement of Spectral and Spatial Resolution." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 1099–103. http://dx.doi.org/10.5194/isprsarchives-xl-8-1099-2014.

Full text
Abstract:
Hyperspectral image enhancement has been a concern for the remote sensing society for detailed end member detection. Hyperspectral remote sensor collects images in hundreds of narrow, continuous spectral channels, whereas multispectral remote sensor collects images in relatively broader wavelength bands. However, the spatial resolution of the hyperspectral sensor image is comparatively lower than that of the multispectral. As a result, spectral signatures from different end members originate within a pixel, known as mixed pixels. This paper presents an approach for obtaining an image which has the spatial resolution of the multispectral image and spectral resolution of the hyperspectral image, by fusion of hyperspectral and multispectral image. The proposed methodology also addresses the band remapping problem, which arises due to different regions of spectral coverage by multispectral and hyperspectral images. Therefore we apply algorithms to restore the spatial information of the hyperspectral image by fusing hyperspectral bands with only those bands which come under each multispectral band range. The proposed methodology is applied over Henry Island, of the Sunderban eco-geographic province. The data is collected by the Hyperion hyperspectral sensor and LISS IV multispectral sensor.
APA, Harvard, Vancouver, ISO, and other styles
2

Mifdal, Jamila, Bartomeu Coll, Jacques Froment, and Joan Duran. "Variational Fusion of Hyperspectral Data by Non-Local Filtering." Mathematics 9, no. 11 (2021): 1265. http://dx.doi.org/10.3390/math9111265.

Full text
Abstract:
The fusion of multisensor data has attracted a lot of attention in computer vision, particularly among the remote sensing community. Hyperspectral image fusion consists in merging the spectral information of a hyperspectral image with the geometry of a multispectral one in order to infer an image with high spatial and spectral resolutions. In this paper, we propose a variational fusion model with a nonlocal regularization term that encodes patch-based filtering conditioned to the geometry of the multispectral data. We further incorporate a radiometric constraint that injects the high frequencies of the scene into the fused product with a band per band modulation according to the energy levels of the multispectral and hyperspectral images. The proposed approach proved robust to noise and aliasing. The experimental results demonstrate the performance of our method with respect to the state-of-the-art techniques on data acquired by commercial hyperspectral cameras and Earth observation satellites.
APA, Harvard, Vancouver, ISO, and other styles
3

Gao, Jianhao, Jie Li, and Menghui Jiang. "Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner." Remote Sensing 13, no. 16 (2021): 3226. http://dx.doi.org/10.3390/rs13163226.

Full text
Abstract:
Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images. An intelligent idea to obtain high-resolution hyperspectral images is hyperspectral and multispectral image fusion. In recent years, many studies have found that deep learning-based fusion methods outperform the traditional fusion methods due to the strong non-linear fitting ability of convolution neural network. However, the function of deep learning-based methods heavily depends on the size and quality of training dataset, constraining the application of deep learning under the situation where training dataset is not available or of low quality. In this paper, we introduce a novel fusion method, which operates in a self-supervised manner, to the task of hyperspectral and multispectral image fusion without training datasets. Our method proposes two constraints constructed by low-resolution hyperspectral images and fake high-resolution hyperspectral images obtained from a simple diffusion method. Several simulation and real-data experiments are conducted with several popular remote sensing hyperspectral data under the condition where training datasets are unavailable. Quantitative and qualitative results indicate that the proposed method outperforms those traditional methods by a large extent.
APA, Harvard, Vancouver, ISO, and other styles
4

Li, Jiaxin, Ke Zheng, Jing Yao, Lianru Gao, and Danfeng Hong. "Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1–5. http://dx.doi.org/10.1109/lgrs.2022.3151779.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Nikolakopoulos, K., Ev Gioti, G. Skianis, and D. Vaiopoulos. "AMELIORATING THE SPATIAL RESOLUTION OF HYPERION HYPERSPECTRAL DATA. THE CASE OF ANTIPAROS ISLAND." Bulletin of the Geological Society of Greece 43, no. 3 (2017): 1627. http://dx.doi.org/10.12681/bgsg.11337.

Full text
Abstract:
In this study seven fusion techniques and more especially the Ehlers, Gram-Schmidt, High Pass Filter, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Pansharp and PCA, were used for the fusion of Hyperion hyperspectral data with ALI panchromatic data. The panchromatic data have a spatial resolution of 10m while the hyperspectral data have a spatial resolution of 30m. All the fusion techniques are designed for use with classical multispectral data. Thus, it is quite interesting to investigate the assessment of the common used fusion algorithms with the hyperspectral data. The study area is Antiparos Island in the Aegean Sea.
APA, Harvard, Vancouver, ISO, and other styles
6

Chang, Chein-I., Meiping Song, Chunyan Yu, et al. "Editorial for Special Issue “Advances in Hyperspectral Data Exploitation”." Remote Sensing 14, no. 20 (2022): 5111. http://dx.doi.org/10.3390/rs14205111.

Full text
Abstract:
Hyperspectral imaging (HSI) has emerged as a promising, advanced technology in remote sensing and has demonstrated great potential in the exploitation of a wide variety of data. In particular, its capability has expanded from unmixing data samples and detecting targets at the subpixel scale to finding endmembers, which generally cannot be resolved by multispectral imaging. Accordingly, a wealth of new HSI research has been conducted and reported in the literature in recent years. The aim of this Special Issue “Advances in Hyperspectral Data Exploitation“ is to provide a forum for scholars and researchers to publish and share their research ideas and findings to facilitate the utility of hyperspectral imaging in data exploitation and other applications. With this in mind, this Special Issue accepted and published 19 papers in various areas, which can be organized into 9 categories, including I: Hyperspectral Image Classification, II: Hyperspectral Target Detection, III: Hyperspectral and Multispectral Fusion, IV: Mid-wave Infrared Hyperspectral Imaging, V: Hyperspectral Unmixing, VI: Hyperspectral Sensor Hardware Design, VII: Hyperspectral Reconstruction, VIII: Hyperspectral Visualization, and IX: Applications.
APA, Harvard, Vancouver, ISO, and other styles
7

Hervieu, Alexandre, Arnaud Le Bris, and Clément Mallet. "FUSION OF HYPERSPECTRAL AND VHR MULTISPECTRAL IMAGE CLASSIFICATIONS IN URBAN α–AREAS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 457–64. http://dx.doi.org/10.5194/isprs-annals-iii-3-457-2016.

Full text
Abstract:
An energetical approach is proposed for classification decision fusion in urban areas using multispectral and hyperspectral imagery at distinct spatial resolutions. Hyperspectral data provides a great ability to discriminate land-cover classes while multispectral data, usually at higher spatial resolution, makes possible a more accurate spatial delineation of the classes. Hence, the aim here is to achieve the most accurate classification maps by taking advantage of both data sources at the decision level: spectral properties of the hyperspectral data and the geometrical resolution of multispectral images. More specifically, the proposed method takes into account probability class membership maps in order to improve the classification fusion process. Such probability maps are available using standard classification techniques such as Random Forests or Support Vector Machines. Classification probability maps are integrated into an energy framework where minimization of a given energy leads to better classification maps. The energy is minimized using a graph-cut method called quadratic pseudo-boolean optimization (QPBO) with α-expansion. A first model is proposed that gives satisfactory results in terms of classification results and visual interpretation. This model is compared to a standard Potts models adapted to the considered problem. Finally, the model is enhanced by integrating the spatial contrast observed in the data source of higher spatial resolution (i.e., the multispectral image). Obtained results using the proposed energetical decision fusion process are shown on two urban multispectral/hyperspectral datasets. 2-3% improvement is noticed with respect to a Potts formulation and 3-8% compared to a single hyperspectral-based classification.
APA, Harvard, Vancouver, ISO, and other styles
8

Peng, Mingyuan, Guoyuan Li, Xiaoqing Zhou, et al. "A Registration-Error-Resistant Swath Reconstruction Method of ZY1-02D Satellite Hyperspectral Data Using SRE-ResNet." Remote Sensing 14, no. 22 (2022): 5890. http://dx.doi.org/10.3390/rs14225890.

Full text
Abstract:
ZY1-02D is a Chinese hyperspectral satellite, which is equipped with a visible near-infrared multispectral camera and a hyperspectral camera. Its data are widely used in soil quality assessment, mineral mapping, water quality assessment, etc. However, due to the limitations of CCD design, the swath of hyperspectral data is relatively smaller than multispectral data. In addition, stripe noise and collages exist in hyperspectral data. With the contamination brought by clouds appearing in the scene, the availability is further affected. In order to solve these problems, this article used a swath reconstruction method of a spectral-resolution-enhancement method using ResNet (SRE-ResNet), which is to use wide swath multispectral data to reconstruct hyperspectral data through modeling mappings between the two. Experiments show that the method (1) can effectively reconstruct wide swaths of hyperspectral data, (2) can remove noise existing in the hyperspectral data, and (3) is resistant to registration error. Comparison experiments also show that SRE-ResNet outperforms existing fusion methods in both accuracy and time efficiency; thus, the method is suitable for practical application.
APA, Harvard, Vancouver, ISO, and other styles
9

Guilloteau, Claire, Thomas Oberlin, Olivier Berné, Émilie Habart, and Nicolas Dobigeon. "Simulated JWST Data Sets for Multispectral and Hyperspectral Image Fusion." Astronomical Journal 160, no. 1 (2020): 28. http://dx.doi.org/10.3847/1538-3881/ab9301.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Yokoya, Naoto, Takehisa Yairi, and Akira Iwasaki. "Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion." IEEE Transactions on Geoscience and Remote Sensing 50, no. 2 (2012): 528–37. http://dx.doi.org/10.1109/tgrs.2011.2161320.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources
You might also be interested in the extended bibliographies on the topic 'Hyperspectral and multispectral data fusion' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography