Готові списки джерел за темами / Hyperspectral and multispectral data fusion

Добірка наукової літератури з теми "Hyperspectral and multispectral data fusion"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Hyperspectral and multispectral data fusion"

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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.

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Анотація:
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.
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Mifdal, Jamila, Bartomeu Coll, Jacques Froment, and Joan Duran. "Variational Fusion of Hyperspectral Data by Non-Local Filtering." Mathematics 9, no. 11 (May 31, 2021): 1265. http://dx.doi.org/10.3390/math9111265.

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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.
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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 (August 13, 2021): 3226. http://dx.doi.org/10.3390/rs13163226.

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Анотація:
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.
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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.

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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 (January 24, 2017): 1627. http://dx.doi.org/10.12681/bgsg.11337.

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Анотація:
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.
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Chang, Chein-I., Meiping Song, Chunyan Yu, Yulei Wang, Haoyang Yu, Jiaojiao Li, Lin Wang, Hsiao-Chi Li, and Xiaorun Li. "Editorial for Special Issue “Advances in Hyperspectral Data Exploitation”." Remote Sensing 14, no. 20 (October 13, 2022): 5111. http://dx.doi.org/10.3390/rs14205111.

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Анотація:
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.
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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.

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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.
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Peng, Mingyuan, Guoyuan Li, Xiaoqing Zhou, Chen Ma, Lifu Zhang, Xia Zhang, and Kun Shang. "A Registration-Error-Resistant Swath Reconstruction Method of ZY1-02D Satellite Hyperspectral Data Using SRE-ResNet." Remote Sensing 14, no. 22 (November 21, 2022): 5890. http://dx.doi.org/10.3390/rs14225890.

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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.
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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 (June 18, 2020): 28. http://dx.doi.org/10.3847/1538-3881/ab9301.

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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 (February 2012): 528–37. http://dx.doi.org/10.1109/tgrs.2011.2161320.

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Дисертації з теми "Hyperspectral and multispectral data fusion"

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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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Анотація:
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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2

Ahn, Byung Joon. "Design and development of a work-in-progress, low-cost Earth Observation multispectral satellite for use on the International Space Station." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587426345809705.

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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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Анотація:
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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Benhalouche, Fatima Zohra. "Méthodes de démélange et de fusion des images multispectrales et hyperspectrales de télédétection spatiale." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30083/document.

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Au cours de cette thèse, nous nous sommes intéressés à deux principales problématiques de la télédétection spatiale de milieux urbains qui sont : le "démélange spectral " et la "fusion". Dans la première partie de la thèse, nous avons étudié le démélange spectral d'images hyperspectrales de scènes de milieux urbains. Les méthodes développées ont pour objectif d'extraire, d'une manière non-supervisée, les spectres des matériaux présents dans la scène imagée. Le plus souvent, les méthodes de démélange spectral (méthodes dites de séparation aveugle de sources) sont basées sur le modèle de mélange linéaire. Cependant, lorsque nous sommes en présence de paysage non-plat, comme c'est le cas en milieu urbain, le modèle de mélange linéaire n'est plus valide et doit être remplacé par un modèle de mélange non-linéaire. Ce modèle non-linéaire peut être réduit à un modèle de mélange linéaire-quadratique/bilinéaire. Les méthodes de démélange spectral proposées sont basées sur la factorisation matricielle avec contrainte de non-négativité, et elles sont conçues pour le cas particulier de scènes urbaines. Les méthodes proposées donnent généralement de meilleures performances que les méthodes testées de la littérature. La seconde partie de cette thèse à été consacrée à la mise en place de méthodes qui permettent la fusion des images multispectrale et hyperspectrale, afin d'améliorer la résolution spatiale de l'image hyperspectrale. Cette fusion consiste à combiner la résolution spatiale élevée des images multispectrales et la haute résolution spectrale des images hyperspectrales. Les méthodes mises en place sont des méthodes conçues pour le cas particulier de fusion de données de télédétection de milieux urbains. Ces méthodes sont basées sur des techniques de démélange spectral linéaire-quadratique et utilisent la factorisation en matrices non-négatives. Les résultats obtenus montrent que les méthodes développées donnent globalement des performances satisfaisantes pour la fusion des données hyperspectrale et multispectrale. Ils prouvent également que ces méthodes surpassent significativement les approches testées de la littérature
In this thesis, we focused on two main problems of the spatial remote sensing of urban environments which are: "spectral unmixing" and "fusion". In the first part of the thesis, we are interested in the spectral unmixing of hyperspectral images of urban scenes. The developed methods are designed to unsupervisely extract the spectra of materials contained in an imaged scene. Most often, spectral unmixing methods (methods known as blind source separation) are based on the linear mixing model. However, when facing non-flat landscape, as in the case of urban areas, the linear mixing model is not valid any more, and must be replaced by a nonlinear mixing model. This nonlinear model can be reduced to a linear-quadratic/bilinear mixing model. The proposed spectral unmixing methods are based on matrix factorization with non-negativity constraint, and are designed for urban scenes. The proposed methods generally give better performance than the tested literature methods. The second part of this thesis is devoted to the implementation of methods that allow the fusion of multispectral and hyperspectral images, in order to improve the spatial resolution of the hyperspectral image. This fusion consists in combining the high spatial resolution of multispectral images and high spectral resolution of hyperspectral images. The implemented methods are designed for urban remote sensing data. These methods are based on linear-quadratic spectral unmixing techniques and use the non-negative matrix factorization. The obtained results show that the developed methods give good performance for hyperspectral and multispectral data fusion. They also show that these methods significantly outperform the tested literature approaches
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Wahrman, Spencer A. "Time Series Analysis of Vegetation Change using Hyperspectral and Multispectral Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17473.

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Approved for public release; distribution is unlimited
Grand Lake, Colorado has experienced a severe mountain pine beetle outbreak over the past twenty years. The aim of this study was to map lodgepole pine mortality and health decline due to mountain pine beetle. Multispectral data spanning a five-year period from 2006 to 2011 were used to assess the progression from live, green trees to dead, gray-brown trees. IKONOS data from 2011 were corrected to reflectance and validated against an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset, also collected during 2011. These data were used along with additional reflectance-corrected multispectral datasets (IKONOS from 2007 and QuickBird from 2006 and 2009) to create vegetation classification maps using both library spectra and regions of interest. Two sets of classification maps were produced using Mixture-Tuned Matched Filtering. The results were assessed visually and mathematically. Through visual inspection of the classification maps, increasing lodgepole pine mortality over time was observed. The results were quantified using confusion matrices comparing the classification results of the AVIRIS classified data and the IKONOS and QuickBird classified data. The comparison showed that change could be seen over time, but due to the short time period of the data the change was not as significant as expected.
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6

Hall, William D. "Exploration of Data Fusion between Polarimetric Radar and Multispectral Image Data." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17375.

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Approved for public release; distribution is unlimited
Typically, analysis of remote sensing data is limited to one sensor at a time which usually contains data from the same general portion of the electromagnetic spectrum. SAR and visible near infrared data of Monterey, CA, were analyzed and fused with the goal of achieving improved land classification results. A common SAR decomposition, the Pauli decomposition was performed and inspected. The SAR Pauli decomposition and the multispectral reflectance data were fused at the pixel level, then analyzed using multispectral classification techniques. The results were compared to the multispectral classifications using the SAR decomposition results for a basis of interpreting the changes. The combined dataset resulted in little to no quantitative improvement in land cover classification capability, however inspection of the classification maps indicated an improved classification ability with the combined data. The most noticeable increases in classification accuracy occurred in spatial regions where the land features were parallel to the SAR flight line. This dependence on orientation makes this fusion process more ideal for datasets with more consistent features throughout the scene.
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PISCINI, ALESSANDRO. "Neural-Network approach to multispectral and hyperspectral data analysis for volcanic monitoring." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2015. http://hdl.handle.net/2108/214160.

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Adams, Andrew J. "Multispectral persistent surveillance /." Online version of thesis, 2008. http://hdl.handle.net/1850/7070.

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Jahan, Farah. "Fusion of Hyperspectral and LiDAR Data for Land Cover Classification." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/386555.

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Анотація:
Land cover classification has become increasingly important for making the plan to overcome the problems of disorganized and uncontrolled development, the disappearance of prime agricultural lands and deteriorating environmental quality by losing forest, wildlife habitat, wetlands etc. Different remote sensing technologies capture different properties e.g., spectral, shape, etc of ground objects. Nowadays, combined use of multiple remote sensing technologies for land cover classification becomes popular. Spectral image e.g., hyperspectral and lidar point cloud data are commonly used in land cover classification. Among the spectral images, the hyperspectral image contains detailed spectral responses of an object. On the contrary, light detection and ranging (LiDAR) data capture structural information of an object. Thus hyperspectral and LiDAR complement each other by accumulating information from land cover. Several state-of-the-art methods were developed for fusing hyperspectral and LiDAR data for land cover classification where the methods included feature extraction, feature fusion and classification. Still, there are undiscovered properties of both modalities which can contribute significantly in this domain. In this thesis, we discover a number of effective ways for feature extraction from both hyperspectral and LiDAR data. Furthermore, we propose two feature fusion techniques which are able to decrease between-class correlation and increase within-class correlation while fusing features from two modalities. Finally, a decision fusion approach e.g., ensemble classification is incorporated for integrating prediction metrics. In this thesis, we propose three different approaches for separating complex land cover classes by fusing hyperspectral and LiDAR data. The effectiveness of these approaches is validated by experimenting on two datasets e.g., Houston and GU datasets. The Houston dataset is a benchmark dataset that contains fifteen landcover classes and distributed in 2013 IEEE GRSS Data Fusion Contest. On the other hand, GU dataset consists of land cover classes and is prepared from the hyperspectral and LiDAR data collected by the Spectral Imaging Lab of Griffith University. We use two state-of-the-art classifiers e.g., random forest (RF) and support vector machine (SVM) for classifying the features derived by our proposed approaches. In our first approach, we derive eight features from hyperspectral and LiDAR data. Among them two are from hyperspectral and six are from LiDAR data. These eight features show perfect complement property to hyperspectral features. In feature fusion, we explore the effectiveness of layer stacking and principal component analysis (PCA) where effective combination of features is investigated specially for PCA fusion. In our second approach, we integrate three key tasks e.g., band-group fusion, multisource fusion and generic feature (GF) extraction. In band-group fusion, we group hyperspectral bands based on their joint entropy and structural similarity. We apply PCA on each group and retain a few principal components and apply differential attribute profiles (DAP) for extracting spatial features. The spatial and spectral features from individual groups are fused using discriminant correlation analysis (DCA). In multisource fusion, spatial features from hyperspectral and LiDAR are also fused by DCA.We derive eight pixel-wise GF from hyperspectral and LiDAR data which are then arranged sequentially to form an additional feature vector. Finally, we concatenate the features generated by band-group fusion, multi-source fusion and generic feature extraction steps to get a final signature. In our third approach, we propose a novel feature extraction technique named inverse coeffcient of variation (ICV) which explores the Gaussian probability of neighbourhood between every pair of bands in hyperspectral data. We calculate ICV for each band with respect to every other band and form an ICV cube. We derive spatial features (e.g. DAP) from the first few principal components of both hyperspectral and ICV cube. In addition, we derive GF from both hyperspectral and LiDAR data and then spatial features from GF. Secondly, we propose a two-stream fusion approach where canonical correlation analysis (CCA) is used as a basic fusion unit. In one stream pair-wise CCA fusion of spectral features of hyperspectral with spatial features of both hyperspectral and LiDAR takes place. In the other stream, pair-wise CCA fusion of ICV features with spatial features derived from ICV, hyperspectral and LiDAR are performed. Thirdly, an ensemble classification system is designed for decision fusion where features from twostream fusion are distributed into random subsets, and then each subset is transformed for improving feature quality, all are concatenated and classified. This process is executed for several iteration. The final classification results are obtained by weighting and aggregating the prediction metrics given by RF or applying majority voting on the predicted classes given by SVM.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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10

Sharma, Rajeev. "Using multispectral and hyperspectral satellite data for early detection of mountain pine beetle damage." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/31064.

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Анотація:
Mountain pine beetle [MPB] [Dendroctonous ponderosae Hopk.) is the most serious pest of mature lodgepole pine (Pinus contorta) in western North America. Three key research issues important in developing satellite-based methods for early MPB damage detection and mapping are examined in this thesis. Relevant questions relating to these issues are: i) is it possible to provide information on MPB-attacked stands using satellite imagery at an earlier date than conventional methods; ii) is spectral variability in mature lodgepole pine stands significant enough to warrant consideration in MPB attack detection at a landscape level; and iii) are satellite-based hyperspectral bands useful in forest tree species discrimination and early detection of MPB-attacked stands. The first two questions were investigated using multispectral Landsat-7 ETM+ data; the third question was investigated using EO-1 Hyperion hyperspectral data. Using a multi-step deductive approach, MPB-attacked stands were identified with an accuracy of 69% using the Landsat imagery, approximately four months earlier than would be possible with conventional surveys. Significant spectral variability was found in mature stands of lodgepole pine, Douglas-fir (Pseudotsuga menziesii) and spruce (Picea spp.) at the landscape level. Among the three variables examined (stand age, site index and site ecology), site ecology (BEC subzone/variants) had the largest influence on the spectral signatures of the three species. Douglas-fir, lodgepole pine and spruce could be identified with an identification accuracy of 81.8%, 82.1% and 78.9%, respectively, using a subset of nine narrow bands from the Hyperion sensor, mainly distributed in the 1500-1800 nm spectral region. Corresponding accuracies using Landsat data were 66.1%, 74.3% and 67.6%. Another set of nine spectral bands, optimized to identify MPB attack and distributed mainly in the 900-1100 nm spectral region, resulted in identification accuracies of 81.7% and 80.2% for MPB-attacked (mainly green-attack) and unattacked stands, respectively. The results of this thesis demonstrate that early detection of MPB-attacked stands is possible using multispectral and hyperspectral data at a scale and resolution to be of practical use to the forest managers. Some of the results from this study have already been used operationally for planning the harvest of MPB-killed trees.
Forestry, Faculty of
Graduate
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Книги з теми "Hyperspectral and multispectral data fusion"

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Giovanni, Motta, Rizzo Francesco, and Storer James A. 1953-, eds. Hyperspectral data compression. New York: Springer, 2005.

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Chaudhuri, Subhasis. Hyperspectral Image Fusion. New York, NY: Springer New York, 2013.

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1963-, Arora M. K., ed. Advanced image processing techniques for remotely sensed hyperspectral data. Berlin: Springer, 2004.

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Hyperspectral data compression. New York, NY: Springer, 2006.

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5

Hyperspectral data, analysis techniques, and applications. Dehradun: Bishen Singh Mahendra Pal Singh, 2011.

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Chaudhuri, Subhasis, and Ketan Kotwal. Hyperspectral Image Fusion. Springer, 2013.

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Chaudhuri, Subhasis, and Ketan Kotwal. Hyperspectral Image Fusion. Springer, 2015.

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Chang, Chein-I. Hyperspectral Data Exploitation: Theory and Applications. Wiley & Sons, Incorporated, John, 2006.

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Hyperspectral data exploitation: Theory and applications. Hoboken, NJ: Wiley-Interscience, 2007.

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Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas. MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-1265-5.

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Частини книг з теми "Hyperspectral and multispectral data fusion"

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Vibhute, Amol D., Sandeep V. Gaikwad, Rajesh K. Dhumal, Ajay D. Nagne, Amarsinh B. Varpe, Dhananjay B. Nalawade, Karbhari V. Kale, and Suresh C. Mehrotra. "Hyperspectral and Multispectral Remote Sensing Data Fusion for Classification of Complex-Mixed Land Features Using SVM." In Communications in Computer and Information Science, 345–62. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9181-1_31.

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Yang, Jingxiang, Yong-Qiang Zhao, and Jonathan Cheung-Wai Chan. "Hyperspectral–Multispectral Image Fusion Enhancement Based on Deep Learning." In Hyperspectral Image Analysis, 407–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38617-7_14.

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Chen, Wenjing, and Xiaoqiang Lu. "Unregistered Hyperspectral and Multispectral Image Fusion with Synchronous Nonnegative Matrix Factorization." In Pattern Recognition and Computer Vision, 602–14. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60633-6_50.

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Gao, Jianhao, Jie Li, Qiangqiang Yuan, Jiang He, and Xin Su. "Self-supervised Hyperspectral and Multispectral Image Fusion in Deep Neural Network." In Lecture Notes in Computer Science, 425–36. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87361-5_35.

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Wang, Tingting, Yang Xu, Zebin Wu, and Zhihui Wei. "Spatial Spectral Joint Correction Network for Hyperspectral and Multispectral Image Fusion." In Lecture Notes in Computer Science, 16–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-02444-3_2.

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Singh, Harpinder, Ajay Roy, R. K. Setia, and Brijendra Pateriya. "Simulation of Multispectral Data Using Hyperspectral Data for Crop Stress Studies." In Lecture Notes in Electrical Engineering, 43–52. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7698-8_5.

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Liu, Zhe, Yinqiang Zheng, and Xian-Hua Han. "Unsupervised Multispectral and Hyperspectral Image Fusion with Deep Spatial and Spectral Priors." In Computer Vision – ACCV 2020 Workshops, 31–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69756-3_3.

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Zhang, Rui, Peng Fu, Leilei Geng, and Quansen Sun. "Hyperspectral and Multispectral Image Fusion Based on Unsupervised Feature Mixing and Reconstruction Network." In Pattern Recognition and Computer Vision, 189–200. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18916-6_16.

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Chakravortty, Somdatta, and Anil Bhondekar. "Spatial and Spectral Quality Assessment of Fused Hyperspectral and Multispectral Data." In Biologically Rationalized Computing Techniques For Image Processing Applications, 133–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61316-1_7.

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Anuja Srivastava, Vikrant Bhateja, and Aisha Moin. "Combination of PCA and Contourlets for Multispectral Image Fusion." In Proceedings of the International Conference on Data Engineering and Communication Technology, 577–85. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1678-3_55.

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Тези доповідей конференцій з теми "Hyperspectral and multispectral data fusion"

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Winter, Michael E., Edwin M. Winter, Scott G. Beaven, and Anthony J. Ratkowski. "High-performance fusion of multispectral and hyperspectral data." In Defense and Security Symposium, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2006. http://dx.doi.org/10.1117/12.668622.

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Lobato, Michaela, William Robert Norris, Rakesh Nagi, Ahmet Soylemezoglu, and Dustin Nottage. "Machine Learning for Soil Moisture Prediction Using Hyperspectral and Multispectral Data." In 2021 IEEE 24th International Conference on Information Fusion (FUSION). IEEE, 2021. http://dx.doi.org/10.23919/fusion49465.2021.9627067.

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Yokoya, Naoto, and Akira Iwasaki. "Hyperspectral and multispectral data fusion mission on hyperspectral imager suite (HISUI)." In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723731.

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Takeyama, Saori, Shunsuke Ono, and Itsuo Kumazawa. "Hyperspectral and Multispectral Data Fusion by a Regularization Considering." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683646.

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Yokoya, Naoto, Jocelyn Chanussot, and Akira Iwasaki. "Hyperspectral and multispectral data fusion based on nonlinear unmixing." In 2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2012. http://dx.doi.org/10.1109/whispers.2012.6874237.

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Du, Qian, John Ball, and Chiru Ge. "Hyperspectral and LiDAR data fusion using collaborative representation." In Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVI, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2020. http://dx.doi.org/10.1117/12.2558967.

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Xie, Jinchi, Ying Wang, and Jie Li. "Hyperspectral and Multispectral Data Fusion with 1D-Convolution on Spectrum." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884077.

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Hao, Junbo, Ying Wang, Jie Li, and Xinbo Gao. "Non-Local Compressive Network for Hyperspectral and Multispectral Data Fusion." In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019. http://dx.doi.org/10.1109/igarss.2019.8900108.

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Murphy, James M., and Mauro Maggioni. "Diffusion geometric methods for fusion of remotely sensed data." In Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, edited by David W. Messinger and Miguel Velez-Reyes. SPIE, 2018. http://dx.doi.org/10.1117/12.2305274.

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Sun, Airong, and Yihua Tan. "Hyperspectral data classification using image fusion based on curvelet transform." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Henri Maître, Hong Sun, Jianguo Liu, and Enmin Song. SPIE, 2007. http://dx.doi.org/10.1117/12.750049.

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Звіти організацій з теми "Hyperspectral and multispectral data fusion"

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Bissett, W. P., and David D. Kohler. High Resolution Multispectral and Hyperspectral Data Fusion for Advanced Geospatial Information Products. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada630662.

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Bissett, W. P., and David D. Kohler. High Resolution Multispectral and Hyperspectral Data Fusion for Advanced Geospatial Information Products. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada465229.

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Key, Gary, and Mark Schmalz. Surface and Buried Mine Detection with Variance-Based Multispectral Data Fusion. Fort Belvoir, VA: Defense Technical Information Center, November 2000. http://dx.doi.org/10.21236/ada392027.

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West, Roger, David Yocky, John Vander Laan, Dylan Anderson, and Brian Redman. Data Fusion of Very High Resolution Hyperspectral and Polarimetric SAR Imagery for Terrain Classification. Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1813672.

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Mobley, Curtis D. Continued Development of the Look-up-table (LUT) Methodology for Interpretation of Remotely Sensed Ocean Color Data and Fusion of Hyperspectral Imagery with LIDAR Bathymetry. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada630665.

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Cohen, Yafit, Carl Rosen, Victor Alchanatis, David Mulla, Bruria Heuer, and Zion Dar. Fusion of Hyper-Spectral and Thermal Images for Evaluating Nitrogen and Water Status in Potato Fields for Variable Rate Application. United States Department of Agriculture, November 2013. http://dx.doi.org/10.32747/2013.7594385.bard.

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Анотація:
Potato yield and quality are highly dependent on an adequate supply of nitrogen and water. Opportunities exist to use airborne hyperspectral (HS) remote sensing for the detection of spatial variation in N status of the crop to allow more targeted N applications. Thermal remote sensing has the potential to identify spatial variations in crop water status to allow better irrigation management and eventually precision irrigation. The overall objective of this study was to examine the ability of HS imagery in the visible and near infrared spectrum (VIS-NIR) and thermal imagery to distinguish between water and N status in potato fields. To lay the basis for achieving the research objectives, experiments in the US and in Israel were conducted in potato with different irrigation and N-application amounts. Thermal indices based merely on thermal images were found sensitive to water status in both Israel and the US in three potato varieties. Spectral indices based on HS images were found suitable to detect N stress accurately and reliably while partial least squares (PLS) analysis of spectral data was more sensitive to N levels. Initial fusion of HS and thermal images showed the potential of detecting both N stress and water stress and even to differentiate between them. This study is one of the first attempts at fusing HS and thermal imagery to detect N and water stress and to estimate N and water levels. Future research is needed to refine these techniques for use in precision agriculture applications.
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Burks, Thomas F., Victor Alchanatis, and Warren Dixon. Enhancement of Sensing Technologies for Selective Tree Fruit Identification and Targeting in Robotic Harvesting Systems. United States Department of Agriculture, October 2009. http://dx.doi.org/10.32747/2009.7591739.bard.

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The proposed project aims to enhance tree fruit identification and targeting for robotic harvesting through the selection of appropriate sensor technology, sensor fusion, and visual servo-control approaches. These technologies will be applicable for apple, orange and grapefruit harvest, although specific sensor wavelengths may vary. The primary challenges are fruit occlusion, light variability, peel color variation with maturity, range to target, and computational requirements of image processing algorithms. There are four major development tasks in original three-year proposed study. First, spectral characteristics in the VIS/NIR (0.4-1.0 micron) will be used in conjunction with thermal data to provide accurate and robust detection of fruit in the tree canopy. Hyper-spectral image pairs will be combined to provide automatic stereo matching for accurate 3D position. Secondly, VIS/NIR/FIR (0.4-15.0 micron) spectral sensor technology will be evaluated for potential in-field on-the-tree grading of surface defect, maturity and size for selective fruit harvest. Thirdly, new adaptive Lyapunov-basedHBVS (homography-based visual servo) methods to compensate for camera uncertainty, distortion effects, and provide range to target from a single camera will be developed, simulated, and implemented on a camera testbed to prove concept. HBVS methods coupled with imagespace navigation will be implemented to provide robust target tracking. And finally, harvesting test will be conducted on the developed technologies using the University of Florida harvesting manipulator test bed. During the course of the project it was determined that the second objective was overly ambitious for the project period and effort was directed toward the other objectives. The results reflect the synergistic efforts of the three principals. The USA team has focused on citrus based approaches while the Israeli counterpart has focused on apples. The USA team has improved visual servo control through the use of a statistical-based range estimate and homography. The results have been promising as long as the target is visible. In addition, the USA team has developed improved fruit detection algorithms that are robust under light variation and can localize fruit centers for partially occluded fruit. Additionally, algorithms have been developed to fuse thermal and visible spectrum image prior to segmentation in order to evaluate the potential improvements in fruit detection. Lastly, the USA team has developed a multispectral detection approach which demonstrated fruit detection levels above 90% of non-occluded fruit. The Israel team has focused on image registration and statistical based fruit detection with post-segmentation fusion. The results of all programs have shown significant progress with increased levels of fruit detection over prior art.
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Hodul, M., H. P. White, and A. Knudby. A report on water quality monitoring in Quesnel Lake, British Columbia, subsequent to the Mount Polley tailings dam spill, using optical satellite imagery. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/330556.

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In the early morning on the 4th of August 2014, a tailings dam near Quesnel, BC burst, spilling approximately 25 million m3 of runoff containing heavy metal elements into nearby Quesnel Lake (Byrne et al. 2018). The runoff slurry, which included lead, arsenic, selenium, and vanadium spilled through Hazeltine Creek, scouring its banks and picking up till and forest cover on the way, and ultimately ended up in Quesnel Lake, whose water level rose by 1.5 m as a result. While the introduction of heavy metals into Quesnel Lake was of environmental concern, the additional till and forest cover scoured from the banks of Hazeltine Creek added to the lake has also been of concern to salmon spawning grounds. Immediate repercussions of the spill involved the damage of sensitive environments along the banks and on the lake bed, the closing of the seasonal salmon fishery in the lake, and a change in the microbial composition of the lake bed (Hatam et al. 2019). In addition, there appears to be a seasonal resuspension of the tailings sediment due to thermal cycling of the water and surface winds (Hamilton et al. 2020). While the water quality of Quesnel Lake continues to be monitored for the tailings sediments, primarily by members at the Quesnel River Research Centre, the sample-and-test methods of water quality testing used, while highly accurate, are expensive to undertake, and not spatially exhaustive. The use of remote sensing techniques, though not as accurate as lab testing, allows for the relatively fast creation of expansive water quality maps using sensors mounted on boats, planes, and satellites (Ritchie et al. 2003). The most common method for the remote sensing of surface water quality is through the use of a physics-based semianalytical model which simulates light passing through a water column with a given set of Inherent Optical Properties (IOPs), developed by Lee et al. (1998) and commonly referred to as a Radiative Transfer Model (RTM). The RTM forward-models a wide range of water-leaving spectral signatures based on IOPs determined by a mix of water constituents, including natural materials and pollutants. Remote sensing imagery is then used to invert the model by finding the modelled water spectrum which most closely resembles that seen in the imagery (Brando et al 2009). This project set out to develop an RTM water quality model to monitor the water quality in Quesnel Lake, allowing for the entire surface of the lake to be mapped at once, in an effort to easily determine the timing and extent of resuspension events, as well as potentially investigate greening events reported by locals. The project intended to use a combination of multispectral imagery (Landsat-8 and Sentinel-2), as well as hyperspectral imagery (DESIS), combined with field calibration/validation of the resulting models. The project began in the Autumn before the COVID pandemic, with plans to undertake a comprehensive fieldwork campaign to gather model calibration data in the summer of 2020. Since a province-wide travel shutdown and social distancing procedures made it difficult to carry out water quality surveying in a small boat, an insufficient amount of fieldwork was conducted to suit the needs of the project. Thus, the project has been put on hold, and the primary researcher has moved to a different project. This document stands as a report on all of the work conducted up to April 2021, intended largely as an instructional document for researchers who may wish to continue the work once fieldwork may freely and safely resume. This research was undertaken at the University of Ottawa, with supporting funding provided by the Earth Observations for Cumulative Effects (EO4CE) Program Work Package 10b: Site Monitoring and Remediation, Canada Centre for Remote Sensing, through the Natural Resources Canada Research Affiliate Program (RAP).
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