Relevant bibliographies by topics / Remote Sensing Data Fusion (RSDF)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Remote Sensing Data Fusion (RSDF)'

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Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Remote Sensing Data Fusion (RSDF)"

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Ghaffar, M. A. A., T. T. Vu, and T. H. Maul. "MULTI-MODAL REMOTE SENSING DATA FUSION FRAMEWORK." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W2 (July 5, 2017): 85–89. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w2-85-2017.

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The inconsistency between the freely available remote sensing datasets and crowd-sourced data from the resolution perspective forms a big challenge in the context of data fusion. In classical classification problems, crowd-sourced data are represented as points that may or not be located within the same pixel. This discrepancy can result in having mixed pixels that could be unjustly classified. Moreover, it leads to failure in retaining sufficient level of details from data inferences. In this paper we propose a method that can preserve detailed inferences from remote sensing datasets accompanied with crowd-sourced data. We show that advanced machine learning techniques can be utilized towards this objective. The proposed method relies on two steps, firstly we enhance the spatial resolution of the satellite image using Convolutional Neural Networks and secondly we fuse the crowd-sourced data with the upscaled version of the satellite image. However, the covered scope in this paper is concerning the first step. Results show that CNN can enhance Landsat 8 scenes resolution visually and quantitatively.
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Butini, Francesco, Vito Cappellini, and Stefano Fini. "Remote Sensing Data Fusion on Intelligent Terminals." European Transactions on Telecommunications 3, no. 6 (November 1992): 555–63. http://dx.doi.org/10.1002/ett.4460030608.

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Belgiu, Mariana, and Alfred Stein. "Spatiotemporal Image Fusion in Remote Sensing." Remote Sensing 11, no. 7 (April 4, 2019): 818. http://dx.doi.org/10.3390/rs11070818.

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In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.
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Nguyen, Hai, Noel Cressie, and Amy Braverman. "Spatial Statistical Data Fusion for Remote Sensing Applications." Journal of the American Statistical Association 107, no. 499 (September 2012): 1004–18. http://dx.doi.org/10.1080/01621459.2012.694717.

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Wei, Chao, Dong Mei Liu, Fan Wang, and Ling Yan Chen. "Fusion Research of Remote Sensing Image Based on Compressive Sensing." Applied Mechanics and Materials 380-384 (August 2013): 3637–42. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3637.

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Compressive Sensing provides a new method of signal processing, when the image signal is sparse or can be com-pressed, it is possible to substantially lower than the Nyquist sampling rate, the sampling mode of the image signal is sampled, and by recovery algorithms to restore the image signal. This theory can greatly reduce the amount of data calculated in the storage, processing and transmission of the image signal. Based on this theory, the paper presents the method of remote sensing image fusion in compressed sensing domain. Firstly, the image for fast Fourier transform and measurement sampling, namely to obtain the compressed perception domain data, and then using the weighted data fusion, the final fused image is obtained by solving the optimization problem of the reconstructed image. Through the experimental proved that, this fusion method deal less data but fusion effect good.
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Cao, Lei, Jun Liu, and Shu Guang Liu. "Remote Sensing Image Fusion of Worldview-2 Satellite Data." Applied Mechanics and Materials 333-335 (July 2013): 1159–63. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1159.

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In view of the situation that most image fusion methods make spectral distortion more or less; this paper proposes a spatial projection method by introducing the Gaussian scale space theory. According to mechanism of the human visual system represented by the Gaussian scale space, the spatial details feature information are extracted from the original panchromatic (PAN) and multispectral (MS) images, then the feature differences are projected into the original MS image to obtain the fused image. The experimental results with WorldView-2 images show that the proposed method can improve the spatial resolution of the fused MS image effectively, while it can make little spectral distortion to the fused images so as to maintain the great majority spectral information.
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Yao, X. L., S. Y. Sun, X. J. Li, and R. Liu. "A new continuous fusion method of remote sensing data." IOP Conference Series: Earth and Environmental Science 191 (November 5, 2018): 012130. http://dx.doi.org/10.1088/1755-1315/191/1/012130.

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Chen, Yushi, Chunyang Li, Pedram Ghamisi, Xiuping Jia, and Yanfeng Gu. "Deep Fusion of Remote Sensing Data for Accurate Classification." IEEE Geoscience and Remote Sensing Letters 14, no. 8 (August 2017): 1253–57. http://dx.doi.org/10.1109/lgrs.2017.2704625.

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Zhang, Jixian. "Multi-source remote sensing data fusion: status and trends." International Journal of Image and Data Fusion 1, no. 1 (March 2010): 5–24. http://dx.doi.org/10.1080/19479830903561035.

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Schmitt, Michael, and Xiao Xiang Zhu. "Data Fusion and Remote Sensing: An ever-growing relationship." IEEE Geoscience and Remote Sensing Magazine 4, no. 4 (December 2016): 6–23. http://dx.doi.org/10.1109/mgrs.2016.2561021.

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Dissertations / Theses on the topic "Remote Sensing Data Fusion (RSDF)"

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Ghannam, Sherin Ghannam. "Multisensor Multitemporal Fusion for Remote Sensing using Landsat and MODIS Data." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/81092.

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The growing Landsat data archive represents more than four decades of continuous Earth observation. Landsat's role in scientific analysis has increased dramatically in recent years as a result of the open-access policy of the U.S. Geological Survey (USGS). However, this rich data record suffers from relatively low temporal resolution due to the 16-day revisit period of each Landsat satellite. To estimate Landsat images at other points in time, researchers have proposed data-fusion approaches that combine existing Landsat data with images from other sensors, such as MODIS (Moderate Resolution Imaging Spectroradiometer) from the Terra and Aqua satellites. MODIS provides daily revisits, however, with a spatial resolution that is significantly lower than that of Landsat. Fusion of Landsat and MODIS is challenging because of differences in their spatial resolution, band designations, swath width, viewing angle and the noise level. Fusion is even more challenging for heterogeneous landscapes. In the first part of our work, the multiresolution analysis offered by the wavelet transform was explored as a suitable environment for Landsat and MODIS fusion. Our proposed Wavelet-based Spatiotemporal Adaptive Reflectance Fusion Model (WSTARFM) is the first model to merge Landsat and MODIS successfully. It handles the heterogeneity of the landscapes more effectively than the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) does. The system has been tested on simulated data and on actual data of two study areas in North Carolina. For a challenging heterogeneous study area near Greensboro, North Carolina, WSTARFM produced results with median R-squared values of 0.98 and 0.95 for the near-infrared band over deciduous forests and developed areas, respectively. Those results were obtained by withholding an actual Landsat image, and comparing it with a predicted version of the same image. These values represent an improvement over results obtained using the well-known STARFM technique. Similar improvements were obtained for the red band. For the second (homogeneous) study area, WSTARFM produced comparable prediction results to STARFM. In the second part of our work, Landsat-MODIS fusion has been explored from the temporal perspective. The fusion is performed on the Landsat and MODIS per-pixel time series. A new Multisensor Adaptive Time Series Fitting Model (MATSFM) is proposed. MATSFM is the first model to use mapped MODIS values to guide the fitting applied to the sparse Landsat time series. MATSFM produced results with median R-squared of 0.98 over the NDVI images of the first heterogeneous study area compared to 0.97 produced by STARFM. For the second study area, MATSFM also produced better prediction accuracy than STARFM.
Ph. D.
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Kumar, Mrityunjay. "Model based image fusion." Diss., Connect to online resource - MSU authorized users, 2008.

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Necsoiu, Dorel Marius. "A Data Fusion Framework for Floodplain Analysis using GIS and Remotely Sensed Data." Thesis, University of North Texas, 2000. https://digital.library.unt.edu/ark:/67531/metadc2557/.

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Throughout history floods have been part of the human experience. They are recurring phenomena that form a necessary and enduring feature of all river basin and lowland coastal systems. In an average year, they benefit millions of people who depend on them. In the more developed countries, major floods can be the largest cause of economic losses from natural disasters, and are also a major cause of disaster-related deaths in the less developed countries. Flood disaster mitigation research was conducted to determine how remotely sensed data can effectively be used to produce accurate flood plain maps (FPMs), and to identify/quantify the sources of error associated with such data. Differences were analyzed between flood maps produced by an automated remote sensing analysis tailored to the available satellite remote sensing datasets (rFPM), the 100-year flooded areas "predicted" by the Flood Insurance Rate Maps, and FPMs based on DEM and hydrological data (aFPM). Landuse/landcover was also examined to determine its influence on rFPM errors. These errors were identified and the results were integrated in a GIS to minimize landuse / landcover effects. Two substantial flood events were analyzed. These events were selected because of their similar characteristics (i.e., the existence of FIRM or Q3 data; flood data which included flood peaks, rating curves, and flood profiles; and DEM and remote sensing imagery.) Automatic feature extraction was determined to be an important component for successful flood analysis. A process network, in conjunction with domain specific information, was used to map raw remotely sensed data onto a representation that is more compatible with a GIS data model. From a practical point of view, rFPM provides a way to automatically match existing data models to the type of remote sensing data available for each event under investigation. Overall, results showed how remote sensing could contribute to the complex problem of flood management by providing an efficient way to revise the National Flood Insurance Program maps.
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Wilkie, Craig John. "Nonparametric statistical downscaling for the fusion of in-lake and remote sensing data." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8626/.

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Lakes are vital components of the global biosphere, supporting complex ecosystems and playing important roles in the global biogeochemical cycle. However, they are vulnerable to the threat from climate change and their responses to climate forcing, eutrophication and other pressures, and their possibly confounding interactions, are not yet well understood. Monitoring lake health is therefore essential, in order to understand the changing patterns over space and time. Traditionally, in-situ data, which are collected directly from within lakes and analysed in laboratories, have been available for analysis. However, although these data are assumed to be accurate within measurement error, they are expensive to collect, so that few, if any, in-situ sampling locations are available for each lake, often with infrequent sampling at each location. On the other hand, remotely-sensed data, which are derived from reflectance measurements of the Earth's surface, obtained from satellites, have recently become widely available. These data have good spatial coverage of up to 300 metre resolution, covering entire lakes, often with a monthly-average time-scale, but they must firstly be calibrated with the in-situ data to ensure accuracy, before inferences are made. The data for this research were provided by the GloboLakes project (www.globolakes.ac.uk), which is a consortium research project that is investigating the state of lakes and their responses to environmental drivers on a global scale. The research primarily focusses on log(chlorophyll-a) data for Lake Balaton, in Hungary, and for the Great Lakes of North America. The key question of interest for this research is: ``How can data fusion be performed for in-situ and remotely-sensed lake water quality data, accounting for the spatiotemporal change of support between the point-location, point-time in-situ data and the grid-cell-scale, monthly-averaged remotely-sensed data, producing a fused dataset that takes accuracy from the in-situ data and spatial and temporal information from the remotely-sensed data?" In order to answer this question, this thesis presents the following work: An initial analysis of the data for Lake Balaton motivates the following work, by demonstrating the spatial and temporal patterns in the data, using mixed-effects models, generalised additive models, kriging and principal components analysis. Following the identification of statistical downscaling as an appropriate method for fusion of the data, statistical downscaling models are developed, specifically in the framework of Bayesian hierarchical models with spatially-varying coefficients, for the novel application to data for log(chlorophyll-a), producing fully calibrated maps of fused data across lake surfaces, with associated comprehensive uncertainty measures. Bivariate and multiple-lakes statistical downscaling models are developed and applied, motivated by the assumption that sharing information between variables and between lakes can improve the accuracy of model predictions. The statistically novel method of nonparametric statistical downscaling is developed, to account for both the spatial and temporal aspects of the change of support between the in-situ and remotely-sensed data. Using methodology from both functional data analysis and statistical downscaling, the model treats in-situ and remotely-sensed data at each location as observations of smooth functions over time, estimated using bases, with the basis coefficients related via a spatially-varying coefficient regression. This is computed within a Bayesian hierarchical model, enabling the calculation of comprehensive uncertainties. This thesis presents the background, motivation, model development and application of the novel method of nonparametric statistical downscaling, filling the gap in the literature of accounting for changing temporal support in statistical downscaling modelling. Results are presented throughout this thesis, to demonstrate the utility of the method for real lake water quality data.
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Payne, Timothy Myles. "Remote detection using fused data /." Title page, abstract and table of contents only, 1994. http://web4.library.adelaide.edu.au/theses/09PH/09php3465.pdf.

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Brooks, Evan B. "Fourier Series Applications in Multitemporal Remote Sensing Analysis using Landsat Data." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23276.

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Researchers now have unprecedented access to free Landsat data, enabling detailed monitoring of the Earth's land surface and vegetation.  There are gaps in the data, due in part to cloud cover. The gaps are aperiodic and localized, forcing any detailed multitemporal analysis based on Landsat data to compensate.   Harmonic regression approximates Landsat data for any point in time with minimal training images and reduced storage requirements.  In two study areas in North Carolina, USA, harmonic regression approaches were least as good at simulating missing data as STAR-FM for images from 2001.  Harmonic regression had an R^2"0.9 over three quarters of all pixels. It gave the highest R_Predicted^2 values on two thirds of the pixels.  Applying harmonic regression with the same number of harmonics to consecutive years yielded an improved fit, R^2"0.99 for most pixels.   We next demonstrate a change detection method based on exponentially weighted moving average (EWMA) charts of harmonic residuals. In the process, a data-driven cloud filter is created, enabling use of partially clouded data.  The approach is shown capable of detecting thins and subtle forest degradations in Alabama, USA, considerably finer than the Landsat spatial resolution in an on-the-fly fashion, with new images easily incorporated into the algorithm.  EWMA detection accurately showed the location, timing, and magnitude of 85% of known harvests in the study area, verified by aerial imagery.   We use harmonic regression to improve the precision of dynamic forest parameter estimates, generating a robust time series of vegetation index values.  These values are classified into strata maps in Alabama, USA, depicting regions of similar growth potential.  These maps are applied to Forest Service Forest Inventory and Analysis (FIA) plots, generating post-stratified estimates of static and dynamic forest parameters.  Improvements to efficiency for all parameters were such that a comparable random sample would require at least 20% more sampling units, with the improvement for the growth parameter requiring a 50% increase. These applications demonstrate the utility of harmonic regression for Landsat data.  They suggest further applications in environmental monitoring and improved estimation of landscape parameters, critical to improving large-scale models of ecosystems and climate effects.
Ph. D.
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Yang, Bo. "Assimilation of multi-scale thermal remote sensing data using spatio-temporal cokriging method." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868463.

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Piles, Guillem Maria. "Multiscale soil moisture retrievals from microwave remote sensing observations." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/77910.

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La humedad del suelo es la variable que regula los intercambios de agua, energía, y carbono entre la tierra y la atmósfera. Mediciones precisas de humedad son necesarias para una gestión sostenible de los recursos hídricos, para mejorar las predicciones meteorológicas y climáticas, y para la detección y monitorización de sequías e inundaciones. Esta tesis se centra en la medición de la humedad superficial de la Tierra desde el espacio, a escalas global y regional. Estudios teóricos y experimentales han demostrado que la teledetección pasiva de microondas en banda L es optima para la medición de humedad del suelo, debido a que la atmósfera es transparente a estas frecuencias, y a la relación directa de la emisividad del suelo con su contenido de agua. Sin embargo, el uso de la teledetección pasiva en banda L ha sido cuestionado en las últimas décadas, pues para conseguir la resolución temporal y espacial requeridas, un radiómetro convencional necesitaría una gran antena rotatoria, difícil de implementar en un satélite. Actualmente, hay tres principales propuestas para abordar este problema: (i) el uso de un radiómetro de apertura sintética, que es la solución implementada en la misión Soil Moisture and Ocean Salinity (SMOS) de la ESA, en órbita desde noviembre del 2009; (ii) el uso de un radiómetro ligero de grandes dimensiones y un rádar operando en banda L, que es la solución que ha adoptado la misión Soil Moisture Active Passive (SMAP) de la NASA, con lanzamiento previsto en 2014; (iii) el desarrollo de técnicas de desagregación de píxel que permitan mejorar la resolución espacial de las observaciones. La primera parte de la tesis se centra en el estudio del algoritmo de recuperación de humedad del suelo a partir de datos SMOS, que es esencial para obtener estimaciones de humedad con alta precisión. Se analizan diferentes configuraciones con datos simulados, considerando (i) la opción de añadir información a priori de los parámetros que dominan la emisión del suelo en banda L —humedad, rugosidad, temperatura del suelo, albedo y opacidad de la vegetación— con diferentes incertidumbres asociadas, y (ii) el uso de la polarización vertical y horizontal por separado, o del primer parámetro de Stokes. Se propone una configuración de recuperación de humedad óptima para SMOS. La resolución espacial de los radiómetros de SMOS y SMAP (40-50 km) es adecuada para aplicaciones globales, pero limita la aplicación de los datos en estudios regionales, donde se requiere una resolución de 1-10 km. La segunda parte de esta tesis contiene tres novedosas propuestas de mejora de resolución espacial de estos datos: • Se ha desarrollado un algoritmo basado en la deconvolución de los datos SMOS que permite mejorar la resolución espacial de las medidas. Los resultados de su aplicación a datos simulados y a datos obtenidos con un radiómetro aerotransportado muestran que es posible mejorar el producto de resolución espacial y resolución radiométrica de los datos. • Se presenta un algoritmo para mejorar la resolución espacial de las estimaciones de humedad de SMOS utilizando datos MODIS en el visible/infrarrojo. Los resultados de su aplicación a algunas de las primeras imágenes de SMOS indican que la variabilidad espacial de la humedad del suelo se puede capturar a 32, 16 y 8 km. • Un algoritmo basado en detección de cambios para combinar los datos del radiómetro y el rádar de SMAP en un producto de humedad a 10 km ha sido desarrollado y validado utilizando datos simulados y datos experimentales aerotransportados. Este trabajo se ha desarrollado en el marco de las actividades preparatorias de SMOS y SMAP, los dos primeros satélites dedicados a la monitorización de la variación temporal y espacial de la humedad de la Tierra. Los resultados presentados contribuyen a la obtención de estimaciones de humedad del suelo con la precisión y la resolución espacial necesarias para un mejor conocimiento del ciclo del agua y una mejor gestión de los recursos hídricos.
Soil moisture is a key state variable of the Earth's system; it is the main variable that links the Earth's water, energy and carbon cycles. Accurate observations of the Earth's changing soil moisture are needed to achieve sustainable land and water management, and to enhance weather and climate forecasting skill, flood prediction and drought monitoring. This Thesis focuses on measuring the Earth's surface soil moisture from space at global and regional scales. Theoretical and experimental studies have proven that L-band passive remote sensing is optimal for soil moisture sensing due to its all-weather capabilities and the direct relationship between soil emissivity and soil water content under most vegetation covers. However, achieving a temporal and spatial resolution that could satisfy land applications has been a challenge to passive microwave remote sensing in the last decades, since real aperture radiometers would need a large rotating antenna, which is difficult to implement on a spacecraft. Currently, there are three main approaches to solving this problem: (i) the use of an L-band synthetic aperture radiometer, which is the solution implemented in the ESA Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009; (ii) the use of a large lightweight radiometer and a radar operating at L-band, which is the solution adopted by the NASA Soil Moisture Active Passive (SMAP) mission, scheduled for launch in 2014; (iii) the development of pixel disaggregation techniques that could enhance the spatial resolution of the radiometric observations. The first part of this work focuses on the analysis of the SMOS soil moisture inversion algorithm, which is crucial to retrieve accurate soil moisture estimations from SMOS measurements. Different retrieval configurations have been examined using simulated SMOS data, considering (i) the option of adding a priori information from parameters dominating the land emission at L-band —soil moisture, roughness, and temperature, vegetation albedo and opacity— with different associated uncertainties and (ii) the use of vertical and horizontal polarizations separately, or the first Stokes parameter. An optimal retrieval configuration for SMOS is suggested. The spatial resolution of SMOS and SMAP radiometers (~ 40-50 km) is adequate for global applications, but is a limiting factor to its application in regional studies, where a resolution of 1-10 km is needed. The second part of this Thesis contains three novel downscaling approaches for SMOS and SMAP: • A deconvolution scheme for the improvement of the spatial resolution of SMOS observations has been developed, and results of its application to simulated SMOS data and airborne field experimental data show that it is feasible to improve the product of the spatial resolution and the radiometric sensitivity of the observations by 49% over land pixels and by 30% over sea pixels. • A downscaling algorithm for improving the spatial resolution of SMOS-derived soil moisture estimates using higher resolution MODIS visible/infrared data is presented. Results of its application to some of the first SMOS images show the spatial variability of SMOS-derived soil moisture observations is effectively captured at the spatial resolutions of 32, 16, and 8 km. • A change detection approach for combining SMAP radar and radiometer observations into a 10 km soil moisture product has been developed and validated using SMAP-like observations and airborne field experimental data. This work has been developed within the preparatory activities of SMOS and SMAP, the two first-ever satellites dedicated to monitoring the temporal and spatial variation on the Earth's soil moisture. The results presented contribute to get the most out of these vital observations, that will further our understanding of the Earth's water cycle, and will lead to a better water resources management.
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Robbe, Nils [Verfasser]. "Airborne Oil Spill Remote Sensing: Modelling, Analysis and Fusion of Multi-spectral Data / Nils Robbe." Aachen : Shaker, 2005. http://d-nb.info/1186579773/34.

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Radhakrishnan, Aswathnarayan. "A Study on Applying Learning Techniques to Remote Sensing Data." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586901481703797.

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Books on the topic "Remote Sensing Data Fusion (RSDF)"

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IEEE Geoscience and Remote Sensing Society. and International Society for Photogrammetry and Remote Sensing., eds. 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, URBAN 2003: Berlin, 22-23 May 2003, Technical University of Berlin. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2003.

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Sun, Wanxiao. Land-use classification using high resolution satellite imagery: A new information fusion method : an application in Landau, Germany. Mainz: Geographisches Institut der Johannes Gutenberg-Universität, 2004.

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Sapienza"), IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (2001 University of Rome "La. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas: Rome, 8-9 November 2001, University of Rome "La Sapienza.". Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2001.

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International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining (2009 Wuhan, China). International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining: 13-14 October 2009, Wuhan, China. Edited by Liu Yaolin 1960-, Tang Xinming, Wuhan da xue. School of Resource and Environmental Science, China Jiao yu bu, and SPIE (Society). Bellingham, Wash: SPIE, 2009.

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Advisory Group for Aerospace Research and Development. Sensor and Propagation Panel. Symposium. Multi-sensor systems and data fusion for telecommunications, remote sensing and radar =: Les systemes multi-senseurs et le fusionnement des donnees pour les telecommunications, la teledelection et les radars : papers presented at the Sensor and Propagation Panel Symposium held in Lisbon, Portugal, 29 September-2 October 1997. Neuilly sur Seine: Agard, 1998.

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Chang, Ni-Bin, and Kaixu Bai. Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing. CRC Press, 2018. http://dx.doi.org/10.1201/9781315154602.

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Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing. Taylor & Francis Group, 2018.

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Park, Joong Yong. Data fusion techniques for object space classification using airborne laser data and airborne digital photographs. 2002.

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Ieee/Isprs Joint Workshop on Remote Sensing and Data Fusion over Urban Areas: Rome, 8-9 November 2001 University of Rome LA Sapienza. Ieee, 2001.

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Institute Of Electrical and Electronics Engineers. Ieee/Isprs Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, 2001: Rome, 8-9 November 2001, University of Rome "LA Sapienza". Ieee, 2001.

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Book chapters on the topic "Remote Sensing Data Fusion (RSDF)"

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Richards, John A., and Xiuping Jia. "Data Fusion." In Remote Sensing Digital Image Analysis, 293–312. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03978-6_12.

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Waske, Björn, and Jón Atli Benediktsson. "Decision Fusion, Classification of Multisource Data." In Encyclopedia of Remote Sensing, 140–44. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-36699-9_34.

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Üstündağ, Berk. "Data Fusion in Agricultural Information Systems." In Springer Remote Sensing/Photogrammetry, 103–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66387-2_7.

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Polli, Diego, and Fabio Dell’Acqua. "Fusion of Optical and SAR Data for Seismic Vulnerability Mapping of Buildings." In Optical Remote Sensing, 329–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14212-3_15.

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Ranchin, T. "Wavelets for Modeling and Data Fusion in Remote Sensing." In Multisensor Fusion, 351–63. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-010-0556-2_14.

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Puentes, John, Laurent Lecornu, and Basel Solaiman. "Data and Information Quality in Remote Sensing." In Information Fusion and Data Science, 401–21. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03643-0_17.

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Ban, Yifang, and Alexander Jacob. "Fusion of Multitemporal Spaceborne SAR and Optical Data for Urban Mapping and Urbanization Monitoring." In Multitemporal Remote Sensing, 107–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47037-5_6.

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D’Addabbo, Annarita, Alberto Refice, Domenico Capolongo, Guido Pasquariello, and Salvatore Manfreda. "Data Fusion Through Bayesian Methods for Flood Monitoring from Remotely Sensed Data." In Flood Monitoring through Remote Sensing, 181–208. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63959-8_8.

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Bakos, Karoly Livius, Prashanth Reddy Marpu, and Paolo Gamba. "Decision Fusion of Multiple Classifiers for Vegetation Mapping and Monitoring Applications by Means of Hyperspectral Data." In Optical Remote Sensing, 147–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14212-3_9.

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Ochodnicky, Jan. "Data Filtering and Data Fusion in Remote Sensing Systems." In GeoSpatial Visual Analytics, 155–65. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2899-0_12.

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Conference papers on the topic "Remote Sensing Data Fusion (RSDF)"

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Hellwich, Olaf, and Christian Wiedemann. "Multisensor data fusion for automated scene interpretation." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1999. http://dx.doi.org/10.1117/12.373266.

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Aiazzi, Bruno, Luciano Alparone, Stefano Baronti, and Roberto Carla. "Assessment of pyramid-based multisensor image data fusion." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1998. http://dx.doi.org/10.1117/12.331868.

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Lasaponara, Rosa, Antonio Lanorte, Rosa Coluzzi, and Nicola Masini. "Performance evaluation of data fusion techniques for archaeological prospection based on satellite data." In Remote Sensing, edited by Manfred Ehlers and Ulrich Michel. SPIE, 2007. http://dx.doi.org/10.1117/12.738204.

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Bers, Karlheinz, Thorsten Brehm, Helmut Essen, and Klaus J. Jaeger. "Improvements in object recognition by radar and ladar data fusion." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2004. http://dx.doi.org/10.1117/12.579553.

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Crosetto, Michele. "Fusion of optical and radar data for terrain surface reconstruction." In Remote Sensing, edited by Francesco Posa. SPIE, 1998. http://dx.doi.org/10.1117/12.331351.

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Guo, Peng. "Study on Bayesian hierarchal model-based SST data fusion methods." In Remote Sensing, edited by Charles R. Bostater, Jr., Stelios P. Mertikas, Xavier Neyt, and Miguel Velez-Reyes. SPIE, 2010. http://dx.doi.org/10.1117/12.864912.

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Carthel, Craig, Stefano Coraluppi, Raffaele Grasso, and Patrick Grignan. "Fusion of AIS, RADAR, and SAR data for maritime surveillance." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2007. http://dx.doi.org/10.1117/12.737334.

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Zahzah, El-hadi. "Image data fusion by the OWA operators." In Aerospace Remote Sensing '97, edited by Jacky Desachy and Shahram Tajbakhsh. SPIE, 1997. http://dx.doi.org/10.1117/12.295611.

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Johnson, Jay K., and Bryan S. Haley. "Data fusion as a means of sensor evaluation in archaeological applications." In Remote Sensing, edited by Roland Meynart, Steven P. Neeck, Haruhisa Shimoda, Joan B. Lurie, and Michelle L. Aten. SPIE, 2004. http://dx.doi.org/10.1117/12.509252.

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Nikolakopoulos, Konstantinos G. "Eight different fusion techniques for use with very high-resolution data." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2005. http://dx.doi.org/10.1117/12.626796.

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You might also be interested in the extended bibliographies on the topic 'Remote Sensing Data Fusion (RSDF)' for particular source types:
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