SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity

2015 ◽  
Vol 24 (1) ◽  
pp. 359-373 ◽  
Author(s):  
Pierre-Luc St-Charles ◽  
Guillaume-Alexandre Bilodeau ◽  
Robert Bergevin
Keyword(s):  
Change Detection ◽  
Detection Method

A Novel Change Detection Method for Unregistered Optical Satellite Images

2012 ◽  
Vol E95.B (5) ◽  
pp. 1890-1893
Author(s):  
Wang LUO ◽  
Hongliang LI ◽  
Guanghui LIU ◽  
Guan GUI
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Detection Method ◽  
Optical Satellite Images

Gray change detection method for damage monitoring in materials

2015 ◽  
Vol 54 (4) ◽  
pp. 934 ◽  
Author(s):  
Xiaochuan Zhang ◽  
Ge Yang ◽  
Nan Zhan ◽  
Hongwei Ji
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Damage Monitoring

scholarly journals A Kernel‐Based Change Detection Method to Map Shifts in Phytoplankton Communities Measured by Flow Cytometry

2021 ◽  
Author(s):  
Corinne Jones ◽  
Sophie Clayton ◽  
François Ribalet ◽  
E. Virginia Armbrust ◽  
Zaid Harchaoui
Keyword(s):  
Flow Cytometry ◽  
Change Detection ◽  
Detection Method ◽  
Phytoplankton Communities

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.

scholarly journals Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 4673-4687
Author(s):  
Jixiang Zhao ◽  
Shanwei Liu ◽  
Jianhua Wan ◽  
Muhammad Yasir ◽  
Huayu Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Detection Method ◽  
Feature Fusion ◽  
Evidence Theory ◽  
Remote Sensing Image

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
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