Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack.
Bruna Gregory Palm,Dimas I. Alves,Dimas I. Alves,Mats I. Pettersson,Viet T. Vu,Renato Machado,Renato J. Cintra,Renato J. Cintra,Fábio M. Bayer,Patrik Dammert,Hans Hellsten +10 more
TLDR
This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images and indicates that the median method provided the most accurate representation of the true ground.Abstract:
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.read more
Citations
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Journal ArticleDOI
A Wavelength-Resolution SAR Change Detection Method Based on Image Stack through Robust Principal Component Analysis
Lucas P. Ramos,Alexandre B. Campos,Christofer Schwartz,Leonardo Tomazeli Duarte,Dimas I. Alves,Mats I. Pettersson,Viet T. Vu,Renato Machado +7 more
TL;DR: 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 and indicates 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.
Journal ArticleDOI
CNN-Based Change Detection Algorithm for Wavelength-Resolution SAR Images
TL;DR: In this article , an incoherent change detection algorithm (CDA) for wavelength-resolution synthetic aperture radar (SAR) based on convolutional neural networks (CNNs) was proposed.
Journal ArticleDOI
Change Detection Based on Convolutional Neural Networks Using Stacks of Wavelength-Resolution Synthetic Aperture Radar Images
TL;DR: Two supervised change detection algorithms (CDAs) based on convolutional neural networks (CNNs) that use stacks of coregistered wavelength-resolution (WR) synthetic aperture radar (SAR) images to detect changes in an image under monitoring can be presented.
Journal ArticleDOI
Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers
Bruna Gregory Palm,Fábio M. Bayer,Renato Machado,Mats I. Pettersson,Viet T. Vu,Renato J. Cintra +5 more
TL;DR: In this paper, a robust Rayleigh regression model based on a robust estimation process is proposed as a more realistic approach to model this type of data and the proposed approach considered the weighted maximum likelihood method and was submitted to numerical experiments using simulated and measured SAR images.
Journal ArticleDOI
A generalized control chart for anomaly detection in SAR imagery
TL;DR: In this article , a new parameterization of the Burr XII distribution that generalizes some widely used distributions for modeling SAR data is proposed, and based on the reparameterized distribution, a generalized control chart for anomaly detection in digital images is introduced.
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