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Bibliographies thématiques / Purdue University. Laboratory for Applications of Remote Sensing

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Littérature scientifique sur le sujet « Purdue University. Laboratory for Applications of Remote Sensing »

Auteur : Grafiati

Publié le 25 juillet 2024

Mis à jour le 31 juillet 2025

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Articles de revues sur le sujet "Purdue University. Laboratory for Applications of Remote Sensing"

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Beckman, Peter H. "Building the TeraGrid." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 363, no. 1833 (2005): 1715–28. http://dx.doi.org/10.1098/rsta.2005.1602.

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On 1 October 2004, the most ambitious high-performance Grid project in the United States—the TeraGrid—became fully operational. Resources at nine sites—the San Diego Supercomputer Center, the California Institute of Technology, the National Center for Supercomputing Applications, the University of Chicago/Argonne National Laboratory, Pittsburgh Supercomputing Center, Texas Advanced Computing Center, Purdue University, Indiana University and Oak Ridge National Laboratory—were joined via an ultra-fast optical network, unified policies and security procedures and a sophisticated distributed compu

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Potůčková, M., J. Albrechtová, K. Anders, et al. "E-TRAINEE: OPEN E-LEARNING COURSE ON TIME SERIES ANALYSIS IN REMOTE SENSING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 989–96. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-989-2023.

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Abstract. Time series (TS) analysis has always been an important topic in Remote Sensing (RS). Open access to archives of Earth Observation missions, periodically acquired nation-wide aerial imagery and LiDAR point clouds, and various research datasets, together with open tools for TS data processing, have brought new possibilities in RS research but also challenges in education. The topic of TS analysis in RS has become an essential part of MSc curricula in geoinformatics, geography, and related fields. Open learning materials systematically addressing the issue of TS at the master's level ar

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Dąbrowski, R., and A. Jenerowicz. "PORTABLE IMAGERY QUALITY ASSESSMENT TEST FIELD FOR UAV SENSORS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 26, 2015): 117–22. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-117-2015.

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Nowadays the imagery data acquired from UAV sensors are the main source of all data used in various remote sensing applications, photogrammetry projects and in imagery intelligence (IMINT) as well as in other tasks as decision support. Therefore quality assessment of such imagery is an important task. The research team from Military University of Technology, Faculty of Civil Engineering and Geodesy, Geodesy Institute, Department of Remote Sensing and Photogrammetry has designed and prepared special test field- The Portable Imagery Quality Assessment Test Field (PIQuAT) that provides quality as

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Wu, Weicheng, Yalan Liu, and Mingxing Hu. "Editorial on Special Issue “Geo-Information Technology and Its Applications”." ISPRS International Journal of Geo-Information 11, no. 6 (2022): 347. http://dx.doi.org/10.3390/ijgi11060347.

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Geo-information technology plays a critical role in urban planning and management, land resource quantification, natural disaster risk and damage assessment, smart city development, land cover change modeling and touristic flow management. In particular, the development of big data mining and machine learning techniques (including deep learning) in recent years has expanded the potential applications of geo-information technology and promoted innovation in approaches to mining in different fields. In this context, the International Conference on Geo-Information Technology and its Applications

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Bhattacharjee, C., D. Deb, H. S. Das, A. K. Sen, and R. Gupta. "Modelling Laboratory Data of Bidirectional Reflectance of a Regolith Surface Containing Alumina." Publications of the Astronomical Society of Australia 28, no. 3 (2011): 261–65. http://dx.doi.org/10.1071/as10025.

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AbstractBidirectional reflectance of a surface is defined as the ratio of the scattered radiation at the detector to the incident irradiance as a function of geometry. Accurate knowledge of the bidirectional reflection function for layers composed of discrete, randomly positioned scattering particles is essential for many remote sensing, engineering, and biophysical applications, as well as for different areas of astrophysics. Computations of bidirectional reflection functions for plane parallel particulate layers are usually reduced to solving the radiative transfer equation by the existing t

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Hosseiny, B., J. Amini, M. Esmaeilzade, and M. Nekoee. "RANGE MIGRATION ALGORITHM IN THE PROCESSING CHAIN OF SIGNALS OF A GROUND-BASED SAR SENSOR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 521–25. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-521-2019.

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Abstract. Synthetic aperture radar (SAR) system based on frequency modulated continuous wave (FM-CW) transmission is a viable option for producing high-resolution ground-based imaging radars. Compared with pulsed SAR systems, the combination of FM-CW technology and SAR processing techniques have the advantages of small cubage, lightweight, cost-effectiveness, and high resolution in the SAR image. These characteristics make FM-CW SAR suitable to be deployed as payload on ground Based SARs (GB-SARs) for environmental and civilian applications. In this paper, the Range Migration Algorithm (RMA) i

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Dai, Ling, Guangyun Zhang, Jinqi Gong, and Rongting Zhang. "Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data." Applied Sciences 11, no. 21 (2021): 10502. http://dx.doi.org/10.3390/app112110502.

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In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. Th

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Gambardella, Carmine, Rosaria Parente, Alessandro Ciambrone, and Marialaura Casbarra. "A Principal Components Analysis-Based Method for the Detection of Cannabis Plants Using Representation Data by Remote Sensing." Data 6, no. 10 (2021): 108. http://dx.doi.org/10.3390/data6100108.

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Integrating the representation of the territory, through airborne remote sensing activities with hyperspectral and visible sensors, and managing complex data through dimensionality reduction for the identification of cannabis plantations, in Albania, is the focus of the research proposed by the multidisciplinary group of the Benecon University Consortium. In this study, principal components analysis (PCA) was used to remove redundant spectral information from multiband datasets. This makes it easier to identify the most prevalent spectral characteristics in most bands and those that are specif

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Gambardella, Carmine, Rosaria Parente, Alessandro Ciambrone, and Marialaura Casbarra. "A Principal Components Analysis-Based Method for the Detection of Cannabis Plants Using Representation Data by Remote Sensing." Data 6, no. 10 (2021): 108. http://dx.doi.org/10.3390/data6100108.

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Integrating the representation of the territory, through airborne remote sensing activities with hyperspectral and visible sensors, and managing complex data through dimensionality reduction for the identification of cannabis plantations, in Albania, is the focus of the research proposed by the multidisciplinary group of the Benecon University Consortium. In this study, principal components analysis (PCA) was used to remove redundant spectral information from multiband datasets. This makes it easier to identify the most prevalent spectral characteristics in most bands and those that are specif

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Gunawan, Gusta, Dwita Sutjiningsih, Herr Soeryantono, and Soelistiyoweni Widjanarko. "Soil Erosion Prediction Using GIS and Remote Sensing on Manjunto Watershed Bengkulu, Indonesia." JOURNAL OF TROPICAL SOILS 18, no. 2 (2013): 141. http://dx.doi.org/10.5400/jts.2013.v18i2.141-148.

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The study aims to assess the rate of erosion that occurred in Manjunto Watershed and financial loss using Geographic Information System and Remote Sensing. Model used to determine the erosion is E30 models. The basis for the development of this model is to integrate with the slope of the slope between NDVI. The value of NDVI obtained from satellite imagery. Slope factor obtained through the DEM processing. To determine the amount of economic losses caused by erosion used the shadow prices. The amount of nutrients lost converted to fertilizer price. The results showed that the eroded catchment

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Livres sur le sujet "Purdue University. Laboratory for Applications of Remote Sensing"

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International Symposium on Machine Processing of Remotely Sensed Data (11th 1985 Purdue University). Machine processing of remotely sensed data: With special emphasis on quantifying global process : models, sensor systems, and analytical methods : eleventh international symposium, June 25-27, 1985, Purdue University, Laboratory for Applications of Remote Sensing, West Lafayette, Indiana. Purdue Research Foundation, 1985.

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Chapitres de livres sur le sujet "Purdue University. Laboratory for Applications of Remote Sensing"

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Rochon Gilbert L., Biehl Larry, Fall Souleymane, et al. "Real-Time Remote Sensing in Support of Ecosystem Services and Sustainability." In NATO Science for Peace and Security Series - E: Human and Societal Dynamics. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-579-2-217.

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The authors present the evolution and current state-of-the-science, with respect to satellite remote sensing and its diverse applications to ecosystems services and ecosystems sustainability. Ecosystems and the services they provide to humanity are continuously threatened by biogenic and anthropogenic impacts. Remote sensing is but one array of tools, in combination with in situ sensing, laboratory analysis, and computer-based modeling techniques, that can serve to elucidate the spatial, temporal, and spectral characteristics of lacustrine, riverine, palustrine, and oceanic water resources, ve

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Actes de conférences sur le sujet "Purdue University. Laboratory for Applications of Remote Sensing"

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Larar, Allen M., S. Roland Drayson, and Paul B. Hays. "Global Tropospheric and Total Ozone Monitoring With a Double-Etalon Fabry-Perot Interferometer." In Optical Remote Sensing of the Atmosphere. Optica Publishing Group, 1995. http://dx.doi.org/10.1364/orsa.1995.wc6.

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Knowledge of the global scale distribution of atmospheric ozone and its temporal variability can only be achieved through implementation of a satellite-based remote sensing technique. Development of a space-based tropospheric ozone remote sensor has remained a difficult task to accomplish due to the challenges associated with such a measurement. Nevertheless, the ability to monitor the global distribution of tropospheric ozone is of much interest for enhancement of scientific understanding as well as to potentially lessen the ill-health impacts associated with exposure to elevated concentratio

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