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Maximum-entropy remote sampling

Kurt M. Anstreicher, Marcia Fampa, Jon Lee, Joy Williams
2001 Discrete Applied Mathematics  
We consider the "remote-sampling" problem of choosing a subset S, with |S| = s, from a set N of observable random variables, so as to obtain as much information as possible about a set T of target random  ...  We provide two methods for calculating lower bounds on the entropy: (i) a spectral method, and (ii) a continuous nonlinear relaxation.  ...  Note that H T (S)=H (T ∪ S) − H (T ), and H (T ) is a constant, so problem (2) is equivalent to the Maximum-Entropy Remote-Sampling Problem: MERSP : max S ⊂ N |S| = s H (S) − H T (S): (3) In Section  ... 
doi:10.1016/s0166-218x(00)00217-1 fatcat:p6m2yqctm5b4jfrhia2nuvvlyq

Remote sensing inversion of lake water quality parameters based on ensemble modelling

Li Xiaojuan, Huang Mutao, Li Jianbao, P. Zhou, Y. He, R. Weerasinghe
2020 E3S Web of Conferences  
Then, the ensemble remote sensing inversion model of water quality parameters was established based on entropy weight method and error analysis.  ...  In this paper, combined with water quality sampling data and Landsat8 satellite remote sensing image data, the inversion model of Chl-a and TN water quality parameter concentration was constructed based  ...  Figure 1 . 1 11 Chl-a ensemble modeling test sample inversion results based on entropy weight method Figure 1 . 2 12 TN ensemble modeling test sample inversion results based on entropy weight methodFigure  ... 
doi:10.1051/e3sconf/202014302007 fatcat:7wnvi6sdmbbdvkwbq2dvcjl7jy

A New Semi-supervised Classification Method of Hyperspectral Image based on Combining Renyi Entropy and Multinomial Logistic Regression Algorithm

Chunyang Wang, Shuangting Wang, Zengzhang Guo, Liping Wang, Chao Ma
2014 International Journal of Signal Processing, Image Processing and Pattern Recognition  
sensing image. maximum information in hyperspectral image to add to the training samples dataset.  ...  A lot of unlabeled samples are constantly added to the sample data using Renyi entropy algorithm.  ...  Secondly, the entropy of the experimental area is calculated through Renyi entropy calculation method, and the some unlabeled samples of maximum Renyi entropy are selected from the calculation data to  ... 
doi:10.14257/ijsip.2014.7.5.06 fatcat:nbajzsn4engv5njy3jxsnl6sge

Bayesian Inference For Phase Unwrapping Using Conjugate Gradient Method In One And Two Dimensions

Yohei Saika, Hiroki Sakaematsu, Shota Akiyama
2012 Zenodo  
We investigated statistical performance of Bayesian inference using maximum entropy and MAP estimation for several models which approximated wave-fronts in remote sensing using SAR interferometry.  ...  Also, we found that the MAP estimation regarded as a deterministic limit of maximum entropy almost achieved the same performance as the Bayes-optimal solution for the set of wave-fronts.  ...  Therefore, in this study, we tried a Bayesian inference using maximum entropy and maximum of a posteriori (MAP) estimation for one and two dimensional phase unwrapping in remote sensing using SAR interferometry  ... 
doi:10.5281/zenodo.1073070 fatcat:2tbzzvf4ovfunjigal5ox7fh3m

Determining representative sample size for validation of continuous, large continental remote sensing data

Megan L. Blatchford, Chris M. Mannaerts, Yijian Zeng
2021 International Journal of Applied Earth Observation and Geoinformation  
The confidence interval (CI) and maximum entropy probability distribution were used as indicators to represent accuracy.  ...  All continuous datasets showed the same trend of CI and entropy with increasing sample size.  ...  The Principle of Maximum Entropy states that the distribution with the maximum entropy best matches the current state of knowledge and provides a measure of the amount of information needed to represent  ... 
doi:10.1016/j.jag.2020.102235 fatcat:ij5wmj534rhcbirpt2i44d472y

Visual exploration of uncertainty in remote-sensing classification

Frans J.M Van der Wel, Linda C Van der Gaag, Ben G.H Gorte
1998 Computers & Geosciences  
Exploratory analysis of remotely-sensed data aims at acquiring insight as to the stability o f possible classi cations of these data and their information value for speci c applications.  ...  So, if the probabilities PrC = C i j x, i = 1 ; : : : ; n , are uniformly distributed, that is, if for all classes C i we h a ve that PrC = C i j x = 1 n , then the entropy is at maximum.  ...  The quadratic score of the pixel is then: X i=1;:::;n PrC = C i j x 1 , PrC = C i j x This measure exhibits the same behaviour in its minimum and maximum values as does the entropy measure.  ... 
doi:10.1016/s0098-3004(97)00120-9 fatcat:redluocabvda3ljhlli3a5q6de

A Comparative Analysis on the Applicability of Entropy in remote sensing [article]

Dr. S.K. Katiyar, Arun P. V.
2013 arXiv   pre-print
Entropy is the measure of uncertainty in any data and is adopted for maximisation of mutual information in many remote sensing operations.  ...  in context of various remote sensing operations namely thresholding, clustering and registration.  ...  The maximum information is achieved when no a priori knowledge is available, in which case, it results in maximum uncertainty.  ... 
arXiv:1303.6926v1 fatcat:dl7trex4dbgcto4avq6nyyfp3u

Statistical Mechanical Approach to Phase Unwrapping in Remote Sensing Using the Synthetic Aperture Radar Interferometry

Yohei SAIKA, Tatsuya UEZU
2013 Interdisciplinary Information Sciences  
Here, we use the maximizer of the posterior marginal (MPM) estimate for phase unwrapping and maximum entropy for noise reduction from unwrapped wave-fronts.  ...  Also, using the Monte Carlo simulations, we clarify that the method of maximum entropy using an appropriate model prior succeeds in reducing noises from the unwrapped wave-front obtained by the MPM estimate  ...  In the next procedure, we carry out noise reduction using the method of maximum entropy.  ... 
doi:10.4036/iis.2013.73 fatcat:piy6uxne3jhabhoaxeypiiekoa

Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning

Chunyang Wang, Zengzhang Guo, Shuangting Wang, Liping Wang, Chao Ma
2015 Journal of Spectroscopy  
Semisupervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively, has been a new research direction  ...  Secondly, the entropy of the experimental area is calculated through Rényi entropy calculation method that was proposed by Rényi in 1961 [20] , and then some unlabeled samples of maximum Rényi entropy  ...  The new training set which is retrained with the unlabeled samples of maximum Rényi entropy is used for the new classification process.  ... 
doi:10.1155/2015/969185 fatcat:k5rjte4pdnhslhwvtnm7jp4yxa

Using Remote Sensing and in situ Measurements for Efficient Mapping and Optimal Sampling of Coral Reefs

Alberto Candela, Kevin Edelson, Michelle M. Gierach, David R. Thompson, Gail Woodward, David Wettergreen
2021 Frontiers in Marine Science  
To this end, we apply new techniques in remote sensing analysis, probabilistic modeling for coral reef mapping, and decision theory for sample selection.  ...  ) Coral Reef Airborne Laboratory (CORAL) mission as a proxy for in situ samples.  ...  The optimal sampling strategies generate paths, whereas the scuba diving samples were collected throughout multiple days, sometimes by more than one person; hence comparisons may not be fair.  ... 
doi:10.3389/fmars.2021.689489 fatcat:zq37zg2vjvha3cynmor22a6dje

Subgrade Cumulative Plastic Deformation under the Bridge in the Transitional Period of Orbit Dynamics Analysis

Yuanyuan Sun
2017 Chemical Engineering Transactions  
Using the maximum entropy method to calculate bridge image deformation degree, need to use the maximum entropy method to calculate bridge image distortion threshold, in order to gain the degree of deformation  ...  Therefore, we choose SVM to detect river objective: first river samples and background samples are input to the SVM in training.  ... 
doi:10.3303/cet1759084 doaj:e65f4d98a92d4a138fa7cf20e90e0c88 fatcat:rnwaguuxn5ab3lo5yrhs7cicqa

Decorrelation of Satellite Precipitation Estimates in Space and Time

Francisco Tapiador, Cecilia Marcos, Andres Navarro, Alfonso Jiménez-Alcázar, Raul Moreno Galdón, Julia Sanz
2018 Remote Sensing  
Our results show that spatial correlation and RMSE would be little affected at the monthly scale in the constellation, but that the precise location of the maximum of precipitation could be compromised  ...  including the indirectness of infrared-based geostationary estimates, and the low orbit of those microwave instruments capable of providing a more precise measurement but suffering from poor temporal sampling  ...  A 3-h sampling places the maximum over the Gulf of Leon, and a 6-h sampling over mainland France; the errors are noticeable.Remote Sens. 2018,10, Figure 8 . 8 Errors in the location of the maximum  ... 
doi:10.3390/rs10050752 fatcat:by7qcb2l3farzf6qfytrdqkm2e

Semi-supervised remote sensing image classification via maximum entropy

Ayse Naz Erkan, Gustavo Camps-Valls, Yasemin Altun
2010 2010 IEEE International Workshop on Machine Learning for Signal Processing  
SLR is a probabilistic discriminative classifier and a specific instance of the generalized maximum entropy framework with a convex loss function.  ...  Remote sensing image segmentation requires multi-category classification typically with limited number of labeled training samples.  ...  GENERALIZED MAXIMUM ENTROPY FRAMEWORK Generalized Maximum Entropy (MaxEnt) aims to find a distribution that minimizes a divergence, D(p|q) between a target distribution p and a reference distribution q  ... 
doi:10.1109/mlsp.2010.5589199 fatcat:sfzutkmgvzcvzm3hpj325nzhxq

Autonomous science platforms and question-asking machines

Kevin H. Knuth, Julian L. Center
2010 2010 2nd International Workshop on Cognitive Information Processing  
As we become increasingly reliant on remote science platforms, the ability to autonomously and intelligently perform data collection becomes critical.  ...  Computationally, this learning cycle is implemented in software consisting of a Bayesian probability-based inference engine coupled to an entropy-based inquiry engine.  ...  Of particular note is the earlier work on active data selection approach of MacKay [21] , maximum entropy sampling and Bayesian experimental design by Sebastiani and Wynn [23] , [24] , cybernetics by  ... 
doi:10.1109/cip.2010.5604217 dblp:conf/cogip/KnuthC10 fatcat:dmavuba4qndepkg27otnl3ne7q

SUGARCANE CLASSIFICATION OPTIMIZATION METHOD BASED ON HIGH RESOLUTION SATELLITE REMOTE SENSING IMAGE OF LOVÁSZ HINGE

Y. N. He, M. Zhu, Y. Q. He, B. Wu
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
different segmentation task put forward different loss function and different optimization algorithm to improve the accuracy of classification, such as used in the classification task more softmax cross entropy  ...  The accuracy of semantic segmentation has been constantly improved, and it has been widely applied in the fields of automatic driving, medical treatment and remote sensing image classification.Semantic  ...  remote sensing image samples need to be homogenized.  ... 
doi:10.5194/isprs-archives-xlii-3-w10-397-2020 fatcat:at3xj4yfbbcnjkt2z2d4a77mju
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