Image Change Detection and Fusion Using MRF Models

2004 ◽  
pp. 279-307 ◽  
Author(s):  
Teerasit Kasetkasem ◽  
Pramod K. Varshney
Keyword(s):  
Change Detection ◽  
Image Change Detection

SAR Image Change Detection Method Based on Neural-CRF Structure

2021 ◽  
Author(s):  
Jianlong Zhang ◽  
Mengying Cui ◽  
Bin Wang
Keyword(s):  
Change Detection ◽  
Detection Method ◽  
Sar Image ◽  
Image Change Detection

An Image Change Detection Algorithm Based on Multi-Feature Self-Attention Fusion Mechanism UNet Network

2021 ◽  
Author(s):  
Gulnaz Alimjan ◽  
Yiliyaer Jiaermuhamaiti ◽  
Huxidan Jumahong ◽  
Shuangling Zhu ◽  
Pazilat Nurmamat
Keyword(s):  
Change Detection ◽  
Detection Algorithm ◽  
Convolutional Network ◽  
Detection Algorithms ◽  
Multiple Dimensions ◽  
Change Detection Algorithms ◽  
Image Change Detection ◽  
Control Loss ◽  
The Relationship ◽  
Entire Network

Various UNet architecture-based image change detection algorithms promote the development of image change detection, but there are still some defects. First, under the encoder–decoder framework, the low-level features are extracted many times in multiple dimensions, which generates redundant information; second, the relationship between each feature layer is not modeled so sufficiently that it cannot produce the optimal feature differentiation representation. This paper proposes a remote image change detection algorithm based on the multi-feature self-attention fusion mechanism UNet network, abbreviated as MFSAF UNet (multi-feature self-attention fusion UNet). We attempt to add multi-feature self-attention mechanism between the encoder and decoder of UNet to obtain richer context dependence and overcome the two above-mentioned restrictions. Since the capacity of convolution-based UNet network is directly proportional to network depth, and a deeper convolutional network means more training parameters, so the convolution of each layer of UNet is replaced as a separated convolution, which makes the entire network to be lighter and the model’s execution efficiency is slightly better than the traditional convolution operation. In addition to these, another innovation point of this paper is using preference to control loss function and meet the demands for different accuracies and recall rates. The simulation test results verify the validity and robustness of this approach.

scholarly journals SAR image change detection based on equal weight image fusion and adaptive threshold in the NSST domain

2018 ◽  
Vol 51 (1) ◽  
pp. 785-794 ◽  
Author(s):  
Zhou Wenyan ◽  
Jia Zhenhong ◽  
Yinfeng Yu ◽  
Jie Yang ◽  
Nilola Kasabov
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Adaptive Threshold ◽  
Equal Weight ◽  
Sar Image ◽  
Image Change Detection
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