scholarly journals CHANGE DETECTION OF TIME-SERIES 3D POINT CLOUDS USING ROBUST PRINCIPAL COMPONENT ANALYSIS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 163-169
Author(s):  
T. Fuse ◽  
T. Yamano
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Change Detection ◽  
Principal Component ◽  
Point Clouds ◽  
Component Analysis ◽  
Detection Methods ◽  
Robust Principal Component Analysis ◽  
3D Point Clouds ◽  
Elevation Changes

Abstract. The chances of acquiring three-dimensional (3D) point clouds have recently increased with the emergence of laser scanners. Hence, 3D monitoring of various objects through the accumulation of “time-series 3D point clouds,” which are point clouds of the same place at different times, is possible. Change detection is a task that is indispensable in 3D monitoring. One of the most common change detection method of 3D point clouds is simple subtraction between two data. However, this method is vulnerable to various errors. Therefore, change detection methods that are robust to errors are required. In this study, we developed robust principal component analysis, which has become popular in the background modelling of video images, to robustly recognize changes in time-series 3D point clouds. We first applied the proposed method to time-series depth images and confirmed its accuracy. We then applied the method to the digital elevation models of Mt. Unzen, which were acquired between 2003 and 2016, to recognize yearly elevation changes. The results show that the proposed method robustly recognizes elevation changes with a properly set parameter.

scholarly journals Robust normal vector estimation in 3D point clouds through iterative principal component analysis

2020 ◽  
Vol 163 ◽  
pp. 18-35
Author(s):  
Julia Sanchez ◽  
Florence Denis ◽  
David Coeurjolly ◽  
Florent Dupont ◽  
Laurent Trassoudaine ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Point Clouds ◽  
Component Analysis ◽  
Normal Vector ◽  
3D Point Clouds ◽  
Vector Estimation

scholarly journals Change Detection in UWB SAR Images Based on Robust Principal Component Analysis

2020 ◽  
Vol 12 (12) ◽  
pp. 1916 ◽  
Author(s):  
Christofer Schwartz ◽  
Lucas P. Ramos ◽  
Leonardo T. Duarte ◽  
Marcelo da S. Pinho ◽  
Mats I. Pettersson ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Change Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Probability Of Detection ◽  
Ratio Test ◽  
Analysis Tool ◽  
Robust Principal Component Analysis ◽  
Sar Images ◽  
Data Set

This paper addresses the use of a data analysis tool, known as robust principal component analysis (RPCA), in the context of change detection (CD) in ultrawideband (UWB) very high-frequency (VHF) synthetic aperture radar (SAR) images. The method considers image pairs of the same scene acquired at different time instants. The CD method aims to maximize the probability of detection (PD) and minimize the false alarm rate (FAR). Such aim fits into a multiobjective optimization problem, since maximizing the probability of detection generally implies an increase in the number of false alarms. In that sense, varying the RPCA regularization parameter leads to PD variation with respect to FAR, which is known as receiver operating characteristic (ROC) curve. To evaluate the proposed method, the CARABAS-II data set was considered. The experimental results show that RPCA via principal component pursuit (PCP) can provide a good trade-off between PD and FAR. A comparison between the results obtained with the proposed method and a classical CD algorithm based on the likelihood ratio test provides the pros and cons of the proposed method.

scholarly journals Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

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