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 Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2008 ◽  
Author(s):  
Bruna G. Palm ◽  
Dimas I. Alves ◽  
Mats I. Pettersson ◽  
Viet T. Vu ◽  
Renato Machado ◽  
...  
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Reference Image ◽  
Sar Images ◽  
Image Stack ◽  
Military Vehicles ◽  
Wavelength Resolution

This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0.11 / km 2 , when considering military vehicles concealed in a forest.

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.

Change detection method for intensity VHF wavelength-resolution SAR images

2021 ◽  
Author(s):  
Gustavo Henrique Mittmann Voigt ◽  
Dimas Irion Alves ◽  
Crístian Müller ◽  
Renato Machado ◽  
Viet T. Vu ◽  
...  
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Sar Images ◽  
Wavelength Resolution

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 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.

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