Change Detection in SAR Images Segmentation Based on NSCT and Spatial Fuzzy Clustering Approach

2020 ◽  
Author(s):  
Ms. Leena B
Keyword(s):  
Change Detection ◽  
Fuzzy Clustering ◽  
Sar Images ◽  
Clustering Approach

A multiobjective fuzzy clustering method for change detection in SAR images

2016 ◽  
Vol 46 ◽  
pp. 767-777 ◽  
Author(s):  
Hao Li ◽  
Maoguo Gong ◽  
Qiao Wang ◽  
Jia Liu ◽  
Linzhi Su
Keyword(s):  
Change Detection ◽  
Fuzzy Clustering ◽  
Clustering Method ◽  
Sar Images ◽  
Fuzzy Clustering Method

Segmenting SAR Images Using Fuzzy Clustering

1999 ◽  
Author(s):  
Paul R. Kersten ◽  
Roger R. Lee ◽  
Jim S. Verdi ◽  
Ron M. Carvlho ◽  
Stephen P. Yankovich
Keyword(s):  
Fuzzy Clustering ◽  
Sar Images

River ice monitoring and change detection with multi-spectral and SAR images: application over yellow river

2021 ◽  
Author(s):  
Xiuwei Zhang ◽  
Yuanzeng Yue ◽  
Lin Han ◽  
Fei Li ◽  
Xiuzhong Yuan ◽  
...  
Keyword(s):  
Change Detection ◽  
Yellow River ◽  
River Ice ◽  
Sar Images

scholarly journals An Unsupervised Fuzzy Clustering Approach for Early Screening of COVID-19 from Radiological Images

2021 ◽  
pp. 1-1
Author(s):  
Weiping Ding ◽  
Shouvik Chakraborty ◽  
Kalyani Mali ◽  
Sankhadeep Chatterjee ◽  
Janmenjoy Nayak ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Early Screening ◽  
Radiological Images ◽  
Clustering Approach ◽  
Unsupervised Fuzzy Clustering

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 in SAR images based on iterative Otsu

2020 ◽  
Vol 53 (1) ◽  
pp. 331-339
Author(s):  
Shuwen Xu ◽  
Yan Liao ◽  
Xueying Yan ◽  
Gang Zhang
Keyword(s):  
Change Detection ◽  
Sar Images
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