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 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 Patch-Based Change Detection Method for SAR Images with Label Updating Strategy

2021 ◽  
Vol 13 (7) ◽  
pp. 1236
Author(s):  
Yuanjun Shu ◽  
Wei Li ◽  
Menglong Yang ◽  
Peng Cheng ◽  
Songchen Han
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Change Detection ◽  
Detection Method ◽  
Training Data ◽  
Synthetic Aperture ◽  
Learning Method ◽  
Sar Images ◽  
Two Stage ◽  
Second Stage

Convolutional neural networks (CNNs) have been widely used in change detection of synthetic aperture radar (SAR) images and have been proven to have better precision than traditional methods. A two-stage patch-based deep learning method with a label updating strategy is proposed in this paper. The initial label and mask are generated at the pre-classification stage. Then a two-stage updating strategy is applied to gradually recover changed areas. At the first stage, diversity of training data is gradually restored. The output of the designed CNN network is further processed to generate a new label and a new mask for the following learning iteration. As the diversity of data is ensured after the first stage, pixels within uncertain areas can be easily classified at the second stage. Experiment results on several representative datasets show the effectiveness of our proposed method compared with several existing competitive methods.

A Novel SAR Images Change Detection Method Based on Dynamic TUNET-CRF Model

2021 ◽  
Author(s):  
Jianlong Zhang ◽  
Mengying Cui ◽  
Bin Wang ◽  
Chen Chen ◽  
Yang Zhou ◽  
...  
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Sar Images

A variational change detection method for multitemporal SAR images

2014 ◽  
Vol 5 (4) ◽  
pp. 342-351 ◽  
Author(s):  
Yin Chen ◽  
Armin B. Cremers ◽  
Zhiguo Cao
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Sar Images

scholarly journals A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images

2020 ◽  
Vol 12 (18) ◽  
pp. 3057
Author(s):  
Nian Shi ◽  
Keming Chen ◽  
Guangyao Zhou ◽  
Xian Sun
Keyword(s):  
Change Detection ◽  
Space Constraint ◽  
Euclidean Distance ◽  
Detection Method ◽  
Feature Space ◽  
Cross Entropy ◽  
Sar Images ◽  
Feature Spaces ◽  
Gram Matrices ◽  
Squared Euclidean Distance

With the development of remote sensing technologies, change detection in heterogeneous images becomes much more necessary and significant. The main difficulty lies in how to make input heterogeneous images comparable so that the changes can be detected. In this paper, we propose an end-to-end heterogeneous change detection method based on the feature space constraint. First, considering that the input heterogeneous images are in two distinct feature spaces, two encoders with the same structure are used to extract features, respectively. A decoder is used to obtain the change map from the extracted features. Then, the Gram matrices, which include the correlations between features, are calculated to represent different feature spaces, respectively. The squared Euclidean distance between Gram matrices, termed as feature space loss, is used to constrain the extracted features. After that, a combined loss function consisting of the binary cross entropy loss and feature space loss is designed for training the model. Finally, the change detection results between heterogeneous images can be obtained when the model is trained well. The proposed method can constrain the features of two heterogeneous images to the same feature space while keeping their unique features so that the comparability between features can be enhanced and better detection results can be achieved. Experiments on two heterogeneous image datasets consisting of optical and SAR images demonstrate the effectiveness and superiority of the proposed method.

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 Performance assessment of a recent change detection method for homogeneous and heterogeneous images

2015 ◽  
pp. 23-29
Author(s):  
Jorge Prendes ◽  
Marie Chabert ◽  
Frédéric Pascal ◽  
Alain Giros ◽  
Jean-Yves Tourneret
Keyword(s):  
Statistical Model ◽  
Change Detection ◽  
Manifold Learning ◽  
Expectation Maximization ◽  
Learning Strategy ◽  
Detection Method ◽  
Recent Change ◽  
Sar Images ◽  
Estimated Parameters ◽  
Optical Images

A statistical model for detecting changes in remote sensing images has recently been proposed in (Prendes et al., 2014a,b). This model is sufficiently general to be used for homogeneous images acquired by the same kind of sensors (e.g., two optical images from Pléiades satellites, possibly with different acquisition conditions), and for heterogeneous images acquired by different sensors (e.g., an optical image acquired from a Pléiades satellite and a synthetic aperture radar (SAR) image acquired from a TerraSAR-X satellite). This model assumes that each pixel is distributed according to a mixture of distributions depending on the noise properties and on the sensor intensity responses to the actual scene. The parameters of the resulting statistical model can be estimated by using the classical expectation-maximization (EM) algorithm. The estimated parameters are finally used to learn the relationships between the images of interest, via a manifold learning strategy. These relationships are relevant for many image processing applications, particularly those requiring a similarity measure (e.g., image change detection and image registration). The main objective of this paper is to evaluate the performance of a change detection method based on this manifold learning strategy initially introduced in (Prendes et al., 2014a,b). This performance is evaluated by using results obtained with pairs of real optical images acquired from Pléiades satellites and pairs of optical and SAR images.

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