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 Metode ROBPCA (Robust Principal Component Analysis) dan Clara (Clustering Large Area) pada Data dengan Outlier

2020 ◽  
Vol 13 (2) ◽  
pp. 11
Author(s):  
Bekti Endar Susilowati ◽  
Pardomuan Robinson Sihombing
Keyword(s):  
Principal Component Analysis ◽  
Covariance Structure ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Large Area ◽  
Robust Principal Component Analysis ◽  
Data Set ◽  
Silhouette Width ◽  
The World

Principal Component Analysis (PCA) merupakan salah satu analisis multivariat yang digunakan untuk mengganti variable dengan Principal Component yang sedikit jumlahnya namun tidak terlalu banyak informasi yang hilang. Atau dengan kata lain, it used to explain the underlying variance-covariance structure of the large data set of variables through a few linear combination of these variables. PCA sangat dipengaruhi oleh kehadiran outlier karena didasarkan pada matriks kovarian yang sensitive terhadap outlier. Oleh karena itu, pada analisis ini akan digunakan PCA yang robust terhadap outlier yaitu ROBPCA atau PCA Hubert. Selanjutnya, dari Principal Component yang terbentuk digunakan sebagai input (masukan) untuk cluster analysis dengan metode Clara (Clustering Large Area). Clustering Large Area merupakan salah satu metode k-medoids yang robust terhadap outlier dan baik digunakan pada data dalam jumlah besar. Dalam studi kasus terhadap variabel penyusun indeks kebahagiaan berdasarkan The World Happiness Report 2018 dengan metode Clara yang menggunakan jarak manhattan didapatkan nilai rata-rata Overall Average Silhouette Width yang terbaik pada 5 cluster. 

scholarly journals Differentially Deep Subspace Representation for Unsupervised Change Detection of SAR Images

2019 ◽  
Vol 11 (23) ◽  
pp. 2740 ◽  
Author(s):  
Bin Luo ◽  
Chudi Hu ◽  
Xin Su ◽  
Yajun Wang
Keyword(s):  
Principal Component Analysis ◽  
Change Detection ◽  
Principal Component ◽  
Temporal Analysis ◽  
Component Analysis ◽  
Sar Images ◽  
Linear Change ◽  
Non Linear ◽  
Multi Temporal ◽  
Sar Data

Temporal analysis of synthetic aperture radar (SAR) time series is a basic and significant issue in the remote sensing field. Change detection as well as other interpretation tasks of SAR images always involves non-linear/non-convex problems. Complex (non-linear) change criteria or models have thus been proposed for SAR images, instead of direct difference (e.g., change vector analysis) with/without linear transform (e.g., Principal Component Analysis, Slow Feature Analysis) used in optical image change detection. In this paper, inspired by the powerful deep learning techniques, we present a deep autoencoder (AE) based non-linear subspace representation for unsupervised change detection with multi-temporal SAR images. The proposed architecture is built upon an autoencoder-like (AE-like) network, which non-linearly maps the input SAR data into a latent space. Unlike normal AE networks, a self-expressive layer performing like principal component analysis (PCA) is added between the encoder and the decoder, which further transforms the mapped SAR data to mutually orthogonal subspaces. To make the proposed architecture more efficient at change detection tasks, the parameters are trained to minimize the representation difference of unchanged pixels in the deep subspace. Thus, the proposed architecture is namely the Differentially Deep Subspace Representation (DDSR) network for multi-temporal SAR images change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed architecture.

scholarly journals A Wavelength-Resolution SAR Change Detection Method Based on Image Stack through Robust Principal Component Analysis

2021 ◽  
Vol 13 (5) ◽  
pp. 833
Author(s):  
Lucas P. Ramos ◽  
Alexandre B. Campos ◽  
Christofer Schwartz ◽  
Leonardo T. Duarte ◽  
Dimas I. Alves ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Change Detection ◽  
False Alarm ◽  
Detection Method ◽  
Principal Component ◽  
Robust Principal Component Analysis ◽  
Sar Images ◽  
Image Stack ◽  
Wavelength Resolution ◽  
Reference Images

Recently, it was demonstrated that low-frequency wavelength-resolution synthetic aperture radar (SAR) images could be considered to follow an additive mixing model due to their backscatter characteristics. This simplification allows for the use of source separation methods, such as robust principal component analysis (RPCA) via principal component pursuit (PCP), for detecting changes in those images. In this manuscript, a change detection method for wavelength-resolution SAR images based on image stack through RPCA is proposed. The method aims to explore both the temporal and flight heading diversity of a set of wavelength-resolution multitemporal SAR images in order to detect concealed targets in forestry areas. A heuristic based on three rules for better exploring the RPCA results is introduced, and a new configurable parameter for false alarm reduction based on the analysis of image windows is proposed. The method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high-frequency (VHF) SAR system CARABAS-II. Experiments for stacks of four and seven reference images are conducted, and the use of reference images acquired with different flight headings is explored. The results indicate that a gain in performance can be achieved by using large image stacks containing, at least, one image of each possible flight heading of the data set, which can result in a probability of detection (PD) above 99% for a false alarm rate (FAR) as low as one false alarm per three square kilometers. Furthermore, it is demonstrated that high PD and low FAR can be achieved, also considering images from similar flight headings as reference images.

scholarly journals Improved Clutter Removal by Robust Principal Component Analysis for Chaos Through-Wall Imaging Radar

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 25 ◽  
Author(s):  
Li Liu ◽  
Qianqian Chen ◽  
Yinping Han ◽  
Hang Xu ◽  
Jingxia Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Matrix Decomposition ◽  
Component Analysis ◽  
Probability Of Detection ◽  
Target Image ◽  
Robust Principal Component Analysis ◽  
Imaging Radar ◽  
Low Probability ◽  
Wall Imaging

Chaos through-wall imaging radar has attracted wide attention due to its inherent low probability of detection/interception, strong anti-jamming, and high resolution. However, the target response is usually overwhelmed by strong clutter. This paper proposes an imaging-then-decomposition method based on two-stage robust principal component analysis (RPCA) to remove the clutter and recover the target image. The proposed method firstly focuses the energy of the preprocessing data by the back-projection imaging algorithm; then, it performs matrix decomposition on the full and the sparse component of the focused data, in succession, by the RPCA algorithm. Simulation and experimental results show that the proposed method can suppress the clutter dramatically and indicate human targets distinctly. Compared with the traditional methods, it has effectiveness and superiority in improving the signal-to-clutter ratio.

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.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

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