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An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data

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REMOTE SENSING OF ENVIRONMENT 2016年 174卷 258-265页

作者： Shao, Yang Lunetta, Ross S. Wheeler, Brandon Iiames, John S. Campbell, James B. Virginia Tech Coll Nat Resources & Environm Dept Geog 115 Major Williams Hall Blacksburg VA 24061 USA US EPA Natl Exposure Res Lab 109 TW Alexander Dr Res Triangle Pk NC 27711 USA

In this study we compared the Savitzky-Golay, asymmetric Gaussian, double-logistic, Whittaker smoother, and discrete Fourier transformation smoothing algorithms (noise reduction) applied to Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data, to provide continuous phenology data used for land-cover (LC) classifications across the Laurentian Great Lakes Basin (GLB). MODIS 16-day 250 m NDVI imagery for the GLB was used in conjunction with National Land Cover Database (NLCD) from 2001, 2006 and 2011, and the Cropland Data Layers (CDL) from 2011 to 2014 to conduct classification evaluations. Inter-class separability was measured by Jeffries-Matusita (JM) distances between selected cover type pairs (both general classes and specific crops), and intra-class variability was measured by calculating simple Euclidean distance for samples within cover types. For the GLB, we found that the application of a smoothing algorithm significantly reduced image noise compared to the raw data. However, the Jeffries-Matusita (JM) measures for smoothed NDVI temporal profiles resulted in large inconsistencies. Of the five algorithms tested, only the Fourier transformation algorithm and Whittaker smoother improved inter-class separability for corn soybean class pair and significantly improved overall classification accuracy. When compared to the raw NDVI data as input, the overall classification accuracy from the Fourier transformation and Whittaker smoother improved performance by approximately 2-6% for some years. Conversely, the asymmetric Gaussian and double-logistic smoothing algorithms actually led to degradation of classification performance. (C) 2015 Elsevier Inc. All rights reserved.

关键词： MODIS-NDVI Multi-temporal analysis smoothing algorithms Validation

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Evaluating time-series smoothing algorithms for multi-temporal land cover classification

Evaluating time-series smoothing algorithms for multi-tempor...

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作者： Wheeler, Brandon Myles Virginia Tech | University

In this study we applied the asymmetric Gaussian, double-logistic, and Savitzky-Golay filters to MODIS time-series NDVI data to compare the capability of smoothing algorithms in noise reduction for improving land cover classification in the Great Lakes Basin, and providing groundwork to support cyanobacteria and cyanotoxin monitoring efforts. We used inter-class separability and intra-class variability, at varying levels of pixel homogeneity, to evaluate the effectiveness of three smoothing algorithms. Based on these initial tests, the algorithm which returned the best results was used to analyze how image stratification by ecoregion can affect filter performance. MODIS 16-day 250m NDVI imagery of the Great Lakes Basin from 2001-2013 were used in conjunction with National Land Cover Database (NLCD) 2006 and 2011 data, and Cropland Data Layers (CDL) from 2008 to 2013 to conduct these evaluations. Inter-class separability was measured by Jeffries-Matusita (JM) distances between selected land cover classes (both general classes and specific crops), and intra-class variability was measured by calculating simple Euclidean distance for samples within a land cover class. Within the study area, it was found that the application of a smoothing algorithm can significantly reduce image noise, improving both inter-class separability and intra-class variability when compared to the raw data. Of the three filters examined, the asymmetric Gaussian filter consistently returned the highest values of interclass separability, while all three filters performed very similarly for within-class variability. The ecoregion analysis based on the asymmetric Gaussian dataset indicated that the scale of study area can heavily impact within-class separability. The criteria we established have potential for furthering our understanding of the strengths and weaknesses of different smoothing algorithms, thereby improving pre-processing decisions for land cover classification using time-series data.

关键词： Remote sensing smoothing algorithms time-series NDVI MODIS Thesis

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Dual smoothing ionospheric gradient monitoring algorithm for dual-frequency BDS GBAS

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Chinese Journal of Aeronautics 2020年第12期33卷 3395-3404页

作者： Zhipeng WANG Yu YIN Dan SONG Kun FANG Qiang LI Xiao LI National Key Laboratory of CNS/ATM School of Electronic and Information EngineeringBeihang UniversityBeijing 100083China Beijing Hualong Tong Science and Technology Co LtdBeijing 100084China

In this study,a Dual smoothing Ionospheric Gradient Monitor Algorithm(DSIGMA)was developed for Code-Carrier Divergence(CCD)faults of dual-frequency Ground-Based Augmentation Systems(GBAS)based on the Bei Dou Navigation Satellite System(BDS).Divergence-Free(DF)combinations of the signals were used to form test statistics for a dualfrequency ***,the single-frequency DSIGMA was reviewed,which supports the GBAS approach service type D(GAST-D)for protection against the effect of large ionospheric *** single-frequency DSIGMA was used to create a novel input scheme for the dual-frequency DSIGMA by introducing DF *** steady states of the test statistics were also *** monitors were characterized using BDS measurement data,whereby standard deviations of 0.0432 and 0.0639 m for the proposed two test statistics were used to calculate the monitor *** extensive simulation was designed to assess the monitor performance by comparing the Probability of Missed Detection(PMD)according to the differential error with the range domain PMD limits under different fault *** results showed that the proposed algorithm has a higher integrity performance than the single-frequency *** minimum detectable divergence with the same missed probability is less than 50%that of GAST-D.

关键词： Approach Augmentation Integrity Ionosphere-monitoring Satellite navigation smoothing algorithms Steady state Threshold

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Modeling and Signal Processing of Bulk Acoustic Wave Passive Wireless Strain Sensors

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Zou, Xiyue Li, Wen Zhang, Yan Hu, Bin China Special Equipment Inspection & Res Inst Key Lab Nondestruct Testing & Evaluat State Market Beijing 100029 Peoples R China Beihang Univ Dept Mech Engn & Automat Beijing 100191 Peoples R China China Special Equipment Inspect & Res Inst Beijing 100029 Peoples R China

Untethered, battery-less, and chip-less passive wireless strain sensors have been widely investigated to overcome the drawbacks of conventional sensors for structure health monitoring of large civil structures. Although the-state-of-the-art passive wireless sensors enable long-range, high-resolution measurements, the signal processing of these sensors is still a challenging task. Passive wireless sensors require an algorithm to capture their resonant frequencies from noisy signals. In this article, we propose an algorithm based on rational polynomial functions to fit the full waveform of bulk acoustic wave (BAW)-based passive wireless strain sensors. We establish an analytical expression for the signal and simplify it based on multiple constrains. Numerical simulations show that the simplified fitting functions can accurately extract the peak frequency of the resonant signal when these constraints are satisfied. The experimental demonstrations confirm that passive wireless sensors utilizing this algorithm achieve a resolution of 4 mu epsilon and a refresh rate of 7.5 Hz. In addition, we used the proposed algorithm to realize the vibration frequency measurement of a cantilever beam with a first mode around 4 Hz. The proposed method has high accuracy and moderate speed in extracting the resonance frequency of passive wireless sensors, thus making it possible to realize noncontact measurements of strain changes or vibrations in large civil structures.

关键词： Sensors Wireless sensor networks Wireless communication Capacitive sensors Transmitters Resonant frequency Receivers Bulk acoustic wave (BAW) device frequency-domain signal passive wireless sensor smoothing algorithms structural health monitoring

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Extended smoothing joint data association for multi-target tracking in cluttered environments

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IET RADAR SONAR AND NAVIGATION 2020年第4期14卷 564-571页

作者： Memon, Sufyan Ali Kim, Myunggun Shin, Minho Daudpoto, Jawaid Pathan, Dur Muhammad Son, Hungsun MUET Dept Mechatron Engn Jamshoro Pakistan UNIST Mech Aerosp & Nucl Engn 50 Unist Gil Ulsan South Korea

In heavily cluttered environments, it is difficult to estimate the uncertain motion of an unknown number of targets with low detection probabilities. In particular, for tracking multiple targets, standard multi-target data association algorithms such as joint integrated probabilistic data association (JIPDA), face complexity and severely limited applicability due to a combinatorially increasing number of possible measurement-to-track associations. Smoothers refine the target estimates based on future scan information. However, in this complex surveillance scenario, existing smoothing algorithms often fail to track the true target trajectories. To overcome such difficulties, this study proposes a new smoothing joint measurement-to-track association algorithm called fixed-interval smoothing JIPDA for tracking extended target trajectories (FIsJIPDA). The algorithm employs two independent JIPDA filters: forward JIPDA (fJIPDA) and backward JIPDA (bJIPDA). fJIPDA tracks the target state forward in time and is computed after the smoothing is achieved. bJIPDA estimates the target state in the backward time sequence. The numerical simulation is performed in a heavily populated cluttered environment with low target-detection probabilities. The results show better target trajectory accuracy and false-track discrimination performance of FIsJIPDA compared with that of existing algorithms for tracking multiple extended targets.

关键词： filtering theory probability radar tracking clutter target tracking sensor fusion low target-detection probabilities target trajectory accuracy false-track discrimination performance tracking multiple extended targets smoothing joint data association multitarget tracking heavily cluttered environments uncertain motion low detection probabilities multiple targets standard multitarget data association algorithms face complexity combinatorially increasing number measurement-to-track associations future scan information complex surveillance scenario smoothing algorithms smoothing joint measurement-to-track association algorithm fixed-interval smoothing JIPDA extended target trajectories independent JIPDA filters target state heavily populated cluttered environment

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Snow accumulation rendering algorithms introducing image processing in virtual reality 8

Snow accumulation rendering algorithms introducing image pro...

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8th International Conference on Digital Image Processing (ICDIP)

作者： Du, Wei Lei, Xiaoyong Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing 100191 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781510605039;9781510605046

Snow is a kind of complicated objects that has irregular shape. Therefore, the topic of snow three-dimensional visualization is always challenging. The real-time rendering accumulated snow is of great importance to improve reality in the scene simulation. In this paper, we put forward an algorithm based on predecessors which introduces Perlin Noise and smoothing algorithms, combining with incline coefficient and exposure coefficient. This algorithm makes snow accumulation more natural. Rendering by combining geometry and texture, complexity of large-scale scene is dropped off sharply. Using occlusion map which is processed by image processing, this algorithm could update occlusion relationship of objects in the scene and get better results. Experiment results show that the proposed algorithm accelerates the rendering and be suitable for large real-time snow accumulated rendering.

关键词： Virtual reality visualization 3-D snow accumulation simulation Perlin Noise smoothing algorithms

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On the relation between Gaussian process quadratures and sigma-point methods

On the relation between Gaussian process quadratures and sig...

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Journal of Advances in Information Fusion

作者： Arkkä, A. Simosä Hartikainen, Jouni Svensson, Lennart Sandblom, Fredrik University Rakentajanaukio 2 Espoo02150 Finland Rocsole Ltd Kuopio70150 Finland Chalmers University of Technology GothenburgSE-412 96 Sweden Volvo Group Trucks Technology GothenburgSE-405 08 Sweden

This article is concerned with Gaussian process quadratures, which are numerical integration methods based on Gaussian process regression methods, and sigma-point methods, which are used in advanced non-linear Kalman filtering and smoothing algorithms. We show that many sigma-point methods can be interpreted as Gaussian process quadrature based methods with suitably selected covariance functions. We show that this interpretation also extends to more general multivariate Gauss-Hermite integration methods and related spherical cubature rules. Additionally, we discuss different criteria for selecting the sigma-point locations: exactness of the integrals of multivariate polynomials up to a given order, minimum average error, and quasi-random point sets. The performance of the different methods is tested in numerical experiments.

关键词： Numerical methods Gaussian distribution Gaussian noise (electronic) Regression analysis Covariance function Gauss hermite integrations Gaussian process regression Gaussian Processes Multivariate polynomial Numerical experiments Numerical integration methods smoothing algorithms

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Newton-based maximum likelihood estimation in nonlinear state space models

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IFAC-PapersOnLine 2015年第28期48卷 398-403页

作者： Kok, Manon Dahlin, Johan Schön, Thomas B. Wills, Adrian Division of Automatic Control Linköping University Sweden Department of Information Technology Uppsala University Sweden School of Engineering University of Newcastle Australia

Maximum likelihood (ML) estimation using Newton's method in nonlinear state space models (SSMs) is a challenging problem due to the analytical intractability of the loglikelihood and its gradient and Hessian. We estimate the gradient and Hessian using Fisher's identity in combination with a smoothing algorithm. We explore two approximations of the log-likelihood and of the solution of the smoothing problem. The first is a linearization approximation which is computationally cheap, but the accuracy typically varies between models. The second is a sampling approximation which is asymptotically valid for any SSM but is more computationally costly. We demonstrate our approach for ML parameter estimation on simulated data from two different SSMs with encouraging results. © 2015

关键词： Maximum likelihood estimation Extended Kalman filters Kalman filters Matrix algebra Maximum likelihood Newton Raphson method Nonlinear analysis Parameter estimation State space methods Fisher's identity Linearization approximation Newton optimizations Newton's methods Nonlinear state space models Particle methods smoothing algorithms smoothing problems

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Comparison of smoothing Techniques and Recognition Methods for Online Kannada Character Recognition System

Comparison of Smoothing Techniques and Recognition Methods f...

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International Conference on Advances in Engineering and Technology Research (ICAETR),

作者： Shwetha, D. Ramya, S. Manipal Inst Technol Dept Elect & Commun Manipal Karnataka India

ISBN: (纸本)9781479963935

This paper aimed at working on Online Recognition of Handwritten Kannada Characters. The recognition was done for the Top, Middle and Bottom strokes of Kannada characters. Genius MousePen i608X was used to collect the handwritten character samples to build the database. Handwritten character samples were collected for each character from a particular target-group which includes people who are native to Kannada language and belong to different age groups. These samples were semi-automatically validated, pre-processed and features were extracted. Segmentation of characters was done to divide the strokes into top stroke, middle stroke and bottom stroke. These segmented strokes were individually processed. The pre-processing techniques used in the project include removal of duplicated points, smoothing, interpolating missing points, resampling of points and size normalization. smoothing techniques was compared for Gaussian and Moving Average smoothing. Dominant point, writing direction and the curvature features were also extracted. In addition to this, recognition was carried out by KNN and SVM pattern recognition methods and a second level of verification rules was incorporated, yielding a maximum recognition rate of 92.5% for KNN and 94.35% for SVM.

关键词： Handwritten Kannada Characters Character Recognition smoothing algorithms KNN SVM

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On the use of a penalized least squares method to process kinematic full-field measurements

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MEASUREMENT SCIENCE AND TECHNOLOGY 2014年第7期25卷 075001-075001页

作者： Moulart, Raphael Rotinat, Rene Arts & Metiers ParisTech F-51006 Chalons Sur Marne France

This work is aimed at exploring the performances of an alternative procedure to smooth and differentiate full-field displacement measurements. After recalling the strategies currently used by the experimental mechanics community, a short overview of the available smoothing algorithms is drawn up and the requirements that such an algorithm has to fulfil to be applicable to process kinematic measurements are listed. A comparative study of the chosen algorithm is performed including the 2D penalized least squares method and two other commonly implemented strategies. The results obtained by penalized least squares are comparable in terms of quality to those produced by the two other algorithms, while the penalized least squares method appears to be the fastest and the most flexible. Unlike both the other considered methods, it is possible with penalized least squares to automatically choose the parameter governing the amount of smoothing to apply. Unfortunately, it appears that this automation is not suitable for the proposed application since it does not lead to optimal strain maps. Finally, it is possible with this technique to perform the derivation to obtain strain maps before smoothing them (while the smoothing is normally applied to displacement maps before the differentiation), which can lead in some cases to a more effective reconstruction of the strain fields.

关键词： full-field displacements maps smoothing algorithms penalized least squares 2D-differentiation generalized cross-validation

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