scholarly journals Change Detection Algorithm Based on U-NET Convolutional Neural Network for Multitemporal Wavelength-Resolution SAR Images

2021 ◽  
Author(s):  
Rodrigo A Santos ◽  
Marcelo S. Pinho ◽  
Renato Machado
Keyword(s):  
Neural Network ◽  
Change Detection ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
Sar Images ◽  
Wavelength Resolution

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.

Local Restricted Convolutional Neural Network for Change Detection in Polarimetric SAR Images

2019 ◽  
Vol 30 (3) ◽  
pp. 818-833 ◽  
Author(s):  
Fang Liu ◽  
Licheng Jiao ◽  
Xu Tang ◽  
Shuyuan Yang ◽  
Wenping Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Change Detection ◽  
Convolutional Neural Network ◽  
Polarimetric Sar ◽  
Sar Images

scholarly journals CNN-Based Change Detection Algorithm for Wavelength-Resolution SAR Images

2020 ◽  
pp. 1-5
Author(s):  
Joao Gabriel Vinholi ◽  
Danilo Silva ◽  
Renato Machado ◽  
Mats I. Pettersson
Keyword(s):  
Change Detection ◽  
Detection Algorithm ◽  
Sar Images ◽  
Wavelength Resolution

scholarly journals A Detection algorithm based on Convolutional Neural Network

2018 ◽  
Author(s):  
Fei Rong ◽  
Li Shasha ◽  
Xu Qingzheng ◽  
Liu Kun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Training Model ◽  
Test Sample ◽  
Training Sample ◽  
Detection Algorithm ◽  
Small Scale ◽  
Detection Model ◽  
Sample Data ◽  
Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

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