Relevant bibliographies by topics / Remote sensing - Mathematics

Contents

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

Academic literature on the topic 'Remote sensing - Mathematics'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Remote sensing - Mathematics"

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Hügel, Max, Holger Rauhut, and Thomas Strohmer. "Remote Sensing via ℓ 1-Minimization." Foundations of Computational Mathematics 14, no. 1 (May 29, 2013): 115–50. http://dx.doi.org/10.1007/s10208-013-9157-9.

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MacBain, John, and Bruce Secrest. "Source Identification in Remote Sensing Problems." SIAM Review 33, no. 1 (March 1991): 109–13. http://dx.doi.org/10.1137/1033007.

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Carr, James R. "Spatial Statistics for Remote Sensing." Mathematical Geology 37, no. 5 (July 2005): 549–50. http://dx.doi.org/10.1007/s11004-005-6672-5.

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Liu, Yun, and Jia-Bao Liu. "Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network." Computational Intelligence and Neuroscience 2022 (May 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/9223552.

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Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were used to optimize the clustering dictionary, and then the image segmentation containing target information is completed. Finally, the image was input into the deep convolution neural network with a dual classifier, and the network detects the abnormal target in the image. The test results of this algorithm show that the structural similarity of the denoised image is higher than 0.86, which shows that this method has good noise reduction performance, image details will not damage, segmentation effect is good, and it can obtain high-definition target image information and accurately detect abnormal targets in the image.
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Xie, Huaming, Qianjiao Wu, Ting Zhang, Zhende Teng, Hao Huang, Ying Shu, Shaoru Feng, and Jing Lou. "A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images." Journal of Mathematics 2021 (December 14, 2021): 1–16. http://dx.doi.org/10.1155/2021/1860160.

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In the complex planting area with scattered parcels, combining the parcel vector data with remote sensing images to extract the winter wheat planting information can make up for the deficiency of the classification from remote sensing images simply. It is a feasible direction for precision agricultural subsidies, but it is difficult to collect large-scale parcel data and obtain high spatial resolution or time-series remote sensing images in mass production. It is a beneficial exploration of making use of existing parcel data generated by the ground survey and medium-resolution remote sensing images with suitable time and spatial resolution to extract winter wheat planting areas for large-scale precision agricultural subsidies. Therefore, this paper proposes a new algorithm to extract winter wheat planting areas based on ownership parcel data and medium-resolution remote sensing images for improving classification accuracy. Initially, the segmentation of the image is carried out. To this end, the parcel data is used to generate the region of interest (ROI) of each parcel. Second, the homogeneity of each ROI is detected by its statistical indices (mean value and standard deviation). Third, the parallelepiped classifier and rule-based feature extraction classification methods are utilized to conduct the homogeneous and nonhomogeneous ROIs. Finally, two classification results are combined as the final classification result. The new algorithm was applied to a complex planting area of 103.60 km2 in central China based on the ownership parcel data and Gaofen-1 PMS and WFV remote sensing images in this paper. The experimental results show that the new algorithm can effectively extract winter wheat planting area, eliminate the problem of salt-and-pepper noise, and obtain high-precision classification results (kappa = 0.9279, overall accuracy = 96.41%, user’s accuracy = 99.16%, producer’s accuracy = 93.39%, commission errors = 0.84%, and omission errors = 6.61%) when the size of ownership parcels matches the spatial resolution of remote sensing images.
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Zhen, Longxia, and Wei Liang. "Planning and Design Method of Multiangle Ecological Building Edge Space under the Background of Rural Revitalization." Mathematical Problems in Engineering 2022 (September 16, 2022): 1–9. http://dx.doi.org/10.1155/2022/2848164.

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Under the background of rural revitalization, in order to realize the planning and design of ecological building edge space, a multi-perspective ecological building edge space planning and design method based on remote sensing image edge segmentation is proposed. The remote sensing visual detection of ecological buildings is realized by fusing multiscale features and multisource scene remote sensing images, and the extracted remote sensing image feature points are calibrated to extract the location information, texture features, super-resolution edge information features, and different levels of change features of the spatial distribution of the edge of ecological buildings. The background difference detection model of an ecological building remote sensing image is established, and the distance of the centroid of the corresponding level is calculated through frame dynamic planning and differential image clustering. Combined with the edge contour detection method of ecological building remote sensing image, the edge space planning and design are realized. The simulation results show that this method has higher accuracy in planning and better accuracy in detecting the contour of ecological building edge space and improves the dynamic planning and positioning ability of multi-perspective ecological building edge space distribution.
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Mamoshin, V. R., and V. I. Nemchinov. "Digital multitariff remote-sensing power supply meters." Measurement Techniques 42, no. 7 (July 1999): 680–82. http://dx.doi.org/10.1007/bf02512091.

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Jordan, A. K., and M. E. Veysoglu. "Electromagnetic remote sensing of sea ice." Inverse Problems 10, no. 5 (October 1, 1994): 1041–58. http://dx.doi.org/10.1088/0266-5611/10/5/004.

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Li, Hongchao, and Fang Wu. "Conversion and Visualization of Remote Sensing Image Data in CAD." Computer-Aided Design and Applications 18, S3 (October 20, 2020): 82–94. http://dx.doi.org/10.14733/cadaps.2021.s3.82-94.

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In this paper, a process visualization model for remote sensing image classification algorithms is constructed to analyze the current processing characteristics of process visualization in remote sensing application systems. The usability of the model is verified in a remote sensing application system with a remote sensing image classification algorithm based on support vector machines as an example. Given the characteristics of remote sensing applications that require high visualization process and a large amount of data processing, the basic process of an image classification algorithm for remote sensing applications is summarized by analyzing the basic process of existing image classification algorithms in remote sensing applications, taking into account the characteristics of process visualization. Based on the existing process of remote sensing image classification algorithm, a process visualization model is proposed. The model takes a goal-based process acts as the basic elements of the model, provides visualization functions and interfaces for human-computer interaction through a human-computer interaction selector, and uses a template knowledge base to save processing data and realize the description of customized processes. The model has little impact on the efficiency and accuracy of the support vector machine-based remote sensing image classification algorithm during the process of process visualization and customization. Finally, the application of the model to integrate business processing of earth observation can address the problem of process customization visualization for remote sensing applications to some extent.
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Pemberton, Joseph C. "Solving Over-Constrained Satellite Remote-Sensing Scheduling Problems." Electronic Notes in Discrete Mathematics 4 (January 2000): 54. http://dx.doi.org/10.1016/s1571-0653(05)80105-2.

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Dissertations / Theses on the topic "Remote sensing - Mathematics"

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Wang, Mingliang. "Distributional modelling in forestry and remote sensing." Thesis, University of Greenwich, 2005. http://gala.gre.ac.uk/6337/.

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The use of distributional models in forestry is investigated, in terms of their capability of modelling distributions of forest mensurational attributes, for modelling and inventory purposes. Emphasis is put on: (i) the univariate and bivariate modelling of tree diameters and heights for stand-level modelling work, and (ii) heuristic methods for use and analysis of distributions which occur in multi-temporal EO imagery, (for the inventory-related tasks of land-use mapping, change detection and growth modelling). In univariate distribution modelling, a new parameterization of the widely-used Johnson’s SB distribution is given, and new Logit-Logistic, generalised Weibull and the Burr system (XII, III, IV) models are introduced into forest modelling. The Logit-Logistic distribution is found to be the best among those compared. The use of regression-based methods of parameter estimation is also investigated. In the domain of bivariate distribution modelling of tree diameters and heights the Plackett method (a particular form of copula) is used to construct Plackett-based bivariate Beta, S­B and Logit-Logistic distributions, (the latter two are new), which are compared with each other and the SBB­ distribution. Other copula functions, including the normal copula, are further employed (for the first time in forest modelling) to construct bivariate distributional models. With the normal copula, the superiority of the Logit-Logistic in the univariate domain is extended into the bivariate domain. To use multi-temporal EO imagery, two pre-processing procedures are necessary: image to image co-registration, and radiometric correction. A spectral correlation-based pixel-matching method is developed to “refine” manually selected control points to achieve very accurate image co-registration. A robust non-parametric method of spectral-distribution standardization is used for relative radiometric correction between images. Finally, possibilities for further research are discussed.
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Phaladi, Shikoane Given. "Using GPS bistatic signal for land and ocean remote sensing in South Africa." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/4920.

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Includes bibliographical references (leaves 73-77).
This project discusses the basic principles and theory of this new technology, and concentrates on reflection points and Fresnel zones. The CPS receivers are placed at different coastal regions within South Africa, and the simulation of the reflection points and Fresnel zones are observed as the CPS satellites pass over South Africa. The East London area was chosen as the location to place the receiver throughout my analysis.
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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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Li, Junhua 1970. "Scale analysis in remote sensing based on wavelet transform and multifractal modeling." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82916.

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With the development of Geographical Information System (GIS) and remote sensing techniques, a great deal of data has provided a set of continuous samples of the earth surface from local, regional to global scales. Several multi-scale, multi-resolution, pyramid or hierarchical methods and statistical methods have been developed and used to investigate the scaling property of remotely sensed data: local variance, texture method, scale variance, semivariogram, and fractal analysis. This research introduces the wavelet transform into the realm of scale study in remote sensing and answers three research questions. Three specific objectives corresponding to the three research questions are answered. They include: (1) exploration of wavelets for scale-dependent analysis of remotely sensed imagery; (2) examination of the relationships between wavelet coefficients and classification accuracy for different resolutions and their improvement of classification accuracy; and (3) multiscaling analysis and stochastic down-scaling of an image by using the wavelet transform and multifractals. The significant results obtained are: (1) Haar wavelets can be used to investigate the scale-dependent and spatial structure of an image and provides another method for selection of optimal sampling size; (2) there is a good relationship between classification accuracy and wavelet coefficients. High/low wavelet coefficient reflects low/high classification accuracy in each land cover type. (3) the maximum likelihood classifier with inclusion of wavelet coefficients can improve land cover classification accuracies. (4) the moment-scale analysis of wavelet coefficients can be used to investigate the multifractal properties of an image. Also the stochastic down-scaling model developed based on wavelet and multifractal generates good simulation results of the fine resolution image.
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Gong, Mengyi. "Statistical methods for sparse image time series of remote-sensing lake environmental measurements." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8608/.

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Remote-sensing technology is widely used in Earth observation, from everyday weather forecasting to long-term monitoring of the air, sea and land. The remarkable coverage and resolution of remote sensing data are extremely beneficial to the investigation of environmental problems, such as the state and function of lakes under climate change. However, the attractive features of remote-sensing data bring new challenges to statistical analysis. The wide coverage and high resolution means that data are usually of large volume. The orbit track of the satellite and the occasional obscuring of the instruments due to atmospheric factors could result in substantial missing observations. Applying conventional statistical methods to this type of data can be ineffective and computationally intensive due to its volume and dimensionality. Modifications to existing methods are often required in order to incorporate the missingness. There is a great need of novel statistical approaches to tackle these challenges. This thesis aims to investigate and develop statistical approaches that can be used in the analysis of the sparse remote-sensing image time series of environmental data. Specifically, three aspects of the data are considered, (a) the high dimensionality, which is associated with the volume and the dimension of data, (b) the sparsity, in the sense of high missing percentages and (c) the spatial/temporal structures, including the patterns and the correlations. Initially, methods for temporal and spatial modelling are explored and implemented with care, e.g. harmonic regression and bivariate spline regression with residual correlation structures. In recognizing the drawbacks of these methods, functional data analysis is employed as a general approach in this thesis. Specifically, functional principal component analysis (FPCA) is used to achieve the goal of dimension reduction. Bivariate basis functions are proposed to transform the satellite image data, which typically consists of thousands/millions of pixels, into functional data with low dimensional representations. This approach has the advantage of identifying spatial variation patterns through the principal component (PC) loadings, i.e. eigenfunctions. To overcome the high missing percentages that might invalidate the standard implementation of the FPCA, the mixed model FPCA (MM-FPCA) was investigated in Chapter 3. Through estimating the PCs using a mixed effect model, the influence of sparsity could be accounted for appropriately. Data imputation can be obtained from the fitted model using the (truncated) Karhunen-Loeve expansion. The method's applicability to sparse image series is examined through a simulation study. To incorporate the temporal dependence into the MM-FPCA, a novel spatio-temporal model consisting of a state space component and a FPCA component is proposed in Chapter 4. The model, referred to as SS-FPCA in the thesis, is developed based on the dynamic spatio-temporal model framework. The SS-FPCA exploits a flexible hierarchical design with (a) a data model consisting of a time varying mean function and random component for the common spatial variation patterns formulated as the FPCA, (b) a process model specifying the type of temporal dynamic of the mean function and (c) a parameter model ensuring the identifiability of the model components. A 2-cycle alternating expectation - conditional maximization (AECM) algorithm is proposed to estimate the SS-FPCA model. The AECM algorithm allows different data augmentations and parameter combinations in various cycles within an iteration, which in this case results in analytical solutions for all the MLEs of model parameters. The algorithm uses the Kalman filter/smoother to update the system states according to the data model and the process model. Model investigations are carried out in Chapter 5, including a simulation study on a 1-dimensional space to assess the performance of the model and the algorithm. This is accompanied by a brief summary of the asymptotic results of the EM-type algorithm, some of which can be used to approximate the standard errors of model estimates. Applications of the MM-FPCA and SS-FPCA to the remote-sensing lake surface water temperature and Chlorophyll data of Lake Victoria (obtained from the European Space Agency's Envisat mission) are presented at the end of Chapter 3 and 5. Remarks on the implications and limitations of these two methods are provided in Chapter 6, along with the potential future extensions of both methods. The Appendices provide some additional theorems, computation and derivation details of the methods investigated in the thesis.
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Demir, Metin A. "Perturbation theory of electromagnetic scattering from layered media with rough interfaces." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1174660001.

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劉文慶 and Wenqing Liu. "Fast tracking of evoked potentials variations by wavelet analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B31243411.

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Propastin, Pavel. "Remote sensing based study on vegetation dynamics in drylands of Kazakhstan." Doctoral thesis, Stuttgart Ibidem-Verl, 2007. http://hdl.handle.net/11858/00-1735-0000-0006-B26A-A.

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Liu, Wenqing. "Fast tracking of evoked potentials variations by wavelet analysis /." Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25205523.

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Osuna, Francisco. "Semi-automated frame transformations using FFT analysis on 2-D Images." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Books on the topic "Remote sensing - Mathematics"

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Mathematical principles of remote sensing. Chelsea, Mich: Sleeping Bear Press, 1999.

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1964-, Lubin Dan, ed. Polar remote sensing. Berlin: Springer, in association with Praxis Publishing, 2004.

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Weng, Qihao. Scale issues in remote sensing. Hoboken, N.J: John Wiley & Sons, 2014.

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Cracknell, Arthur P. Introduction to remote sensing. 2nd ed. Boca Raton, FL: CRC Press, 2005.

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Ladson, Hayes, ed. Introduction to remote sensing. 2nd ed. Boca Raton, FL: CRC Press, 2007.

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Ladson, Hayes, ed. Introduction to remote sensing. London: Taylor & Francis, 1991.

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1958-, Graña Manuel, and Duro Richard J. 1965-, eds. Computational intelligence for remote sensing. Berlin: Springer, 2008.

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Lee, Chulhee. Feature extraction and classification algorithms for high dimensional data. West Lafayette, Ind: School of Electrical Engineering, Purdue University, 1993.

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K, Ghosh S., ed. Remote sensing and geographical information system. Oxford: Alpha Science, 2006.

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Lillesand, Thomas M. Remote sensing and image interpretation. 3rd ed. New York: Wiley, 1994.

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Book chapters on the topic "Remote sensing - Mathematics"

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Wöhler, Christian, and Arne Grumpe. "Integrated DEM Construction and Calibration of Hyperspectral Imagery: A Remote Sensing Perspective." In Mathematics and Visualization, 467–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34141-0_21.

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Gómez, Daniel, Javier Montero, and Gregory Biging. "Improvements to Remote Sensing Using Fuzzy Classification, Graphs and Accuracy Statistics." In Earth Sciences and Mathematics, 1555–75. Basel: Birkhäuser Basel, 2008. http://dx.doi.org/10.1007/978-3-7643-9964-1_6.

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Maaß, P., Christine Böckmann, and Alexander Mekler. "Improvement of Environment Observing Remote Sensing Devices by Regularization Techniques." In Mathematics — Key Technology for the Future, 162–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55753-8_13.

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Maged, Maha, Ahmed Ali Yousef, Haitham Akah, and Essam El-Diwany. "C-Band SIW Slot Synthetic Aperture Radar Antenna for Remote Sensing Applications." In Recent Advances in Engineering Mathematics and Physics, 191–98. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39847-7_15.

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Michalis, Pantelis N. "Wavelet Transform in Remote Sensing with Implementation in Edge Detection and Noise Reduction." In Applications of Mathematics and Informatics in Military Science, 135–50. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4109-0_11.

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Rocchini, Duccio, Luca Delucchi, and Giovanni Bacaro. "The Power of Generalized Entropy for Biodiversity Assessment by Remote Sensing: An Open Source Approach." In Springer Proceedings in Mathematics & Statistics, 211–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73906-9_19.

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Navalgund, Ranganath, and Raghavendra P. Singh. "Remote Sensing." In Encyclopedia of Mathematical Geosciences, 1–4. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_275-1.

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Marinelli, Daniele, Francesca Bovolo, and Lorenzo Bruzzone. "Hyperspectral Remote Sensing." In Encyclopedia of Mathematical Geosciences, 1–6. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-26050-7_155-1.

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Wagner, Wolfgang, and Alexandra von Beringe. "Research Group “Remote Sensing”." In Die Fakultät für Mathematik und Geoinformation, 60–63. Wien: Böhlau Verlag, 2015. http://dx.doi.org/10.7767/9783205202288-033.

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Penrose, Roger, and Martin Gardner. "Mathematics and Reality." In The Emperor's New Mind. Oxford University Press, 1989. http://dx.doi.org/10.1093/oso/9780198519737.003.0011.

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Let us imagine that we have been travelling on a great journey to some far-off world. We shall call this world Tor’Bled-Nam. Our remote sensing device has picked up a signal which is now displayed on a screen in front of us. The image comes into focus and we see (Fig. 3.1): What can it be? Is it some strange-looking insect? Perhaps, instead, it is a dark-coloured lake, with many mountain streams entering it. Or could it be some vast and oddly shaped alien city, with roads going off in various directions to small towns and villages nearby? Maybe it is an island - and then let us try to find whether there is a nearby continent with which it is associated. This we can do by ‘backing away’, reducing the magnification of our sensing device by a linear factor of about fifteen. Lo and behold, the entire world springs into view (Fig. 3.2): Our ‘island’ is seen as a small dot indicated below ‘Fig. 3.1’ in Fig. 3.2. The filaments (streams, roads, bridges?), from the original island all come to an end, with the exception of the one attached at the inside of its right-hand crevice, which finally joins on to the very much larger object that we see depicted in Fig. 3.2. This larger object is clearly similar to the island that we saw first - though it is not precisely the same. If we focus more closely on what appears to be this object’s coastline we see innumerable protuberances - roundish, but themselves possessing similar protuberances of their own. Each small protuberance seems to be attached to a larger one at some minute place, producing many warts upon warts. As the picture becomes clearer, we see myriads of tiny filaments emanating from the structure. The filaments themselves are forked at various places and often meander wildly. At certain spots on the filaments we seem to see little knots of complication which our sensing device, with its present magnification, cannot resolve. Clearly the object is no actual island or continent, nor a landscape of any kind.
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Conference papers on the topic "Remote sensing - Mathematics"

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Bacca, Jorge, Carlos A. Hinojosa, and Henry Arguello. "Kernel Sparse Subspace Clustering with Total Variation Denoising for Hyperspectral Remote Sensing Images." In Mathematics in Imaging. Washington, D.C.: OSA, 2017. http://dx.doi.org/10.1364/math.2017.mtu4c.5.

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Gong, Shunsheng, Guotao Yang, Haolin Yang, Xuewu Cheng, Faquan Li, Yang Dai, and Xiaoyin Li. "Lidar activity at Wuhan Institute of Physics and Mathematics, China." In Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, edited by Upendra N. Singh, Toshikasu Itabe, and Zhishen Liu. SPIE, 2003. http://dx.doi.org/10.1117/12.466596.

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Ma, Hongbin, Yahong Zhao, and Yongsheng Chen. "Road extraction from high resolution remote sensing image based on mathematics morphology." In Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, edited by Lin Liu, Xia Li, Kai Liu, and Xinchang Zhang. SPIE, 2008. http://dx.doi.org/10.1117/12.813225.

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Emetere, Moses Eterigho, T. V. Omotosho, and Kayode Olusola. "On the determination of agricultural prospects using remote sensing and field technique." In PROGRESS IN APPLIED MATHEMATICS IN SCIENCE AND ENGINEERING PROCEEDINGS. AIP Publishing LLC, 2016. http://dx.doi.org/10.1063/1.4940287.

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Fifing, Rudy A. G. Gultom, Boby M. Pratama, and Garnis Anggraeni. "Application of remote sensing monitoring of limestone mining exploitation in Mountain Kendeng." In 2018 2nd Borneo International Conference on Applied Mathematics and Engineering (BICAME). IEEE, 2018. http://dx.doi.org/10.1109/bicame45512.2018.1570495527.

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Supriatna, L., J. Supriatna, R. H. Koetsoer, and N. D. Takarina. "Algorithm model for the determination of Cimandiri Estuarine boundary using remote sensing." In INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES 2015 (ISCPMS 2015): Proceedings of the 1st International Symposium on Current Progress in Mathematics and Sciences. Author(s), 2016. http://dx.doi.org/10.1063/1.4946982.

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Favazza, Fabio, Antonio Gagliano, Michele Mangiameli, and Giuseppe Mussumeci. "Remote sensing to analyse Urban Heating Island. A case study from Catania (Sicily)." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2020. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0081747.

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Berg, D. B., A. N. Medvedev, I. L. Manzhurov, and A. A. Taubaev. "Use of fractal models in the Earth’s remote sensing of the arctic zone." In APPLICATIONS OF MATHEMATICS IN ENGINEERING AND ECONOMICS (AMEE’16): Proceedings of the 42nd International Conference on Applications of Mathematics in Engineering and Economics. Author(s), 2016. http://dx.doi.org/10.1063/1.4968428.

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Hengxing Xie and Shuying Zhao. "Fuzzy mathematics evaluation of atmosphere environmental quality in Shaanxi province." In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5965943.

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Gradinaru, Cristian, Ioan Sorin Herban, and Cristian Gabor. "Development of urban green space monitoring technique with remote sensing and its application." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015). Author(s), 2016. http://dx.doi.org/10.1063/1.4952127.

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