Change detection based on the high-resolution remote sensing images

2005 ◽  
Author(s):  
Junqi Zhou ◽  
Jiabing Sun ◽  
Hong Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Remote Sensing Images

scholarly journals MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4528
Author(s):  
Xin Yang ◽  
Lei Hu ◽  
Yongmei Zhang ◽  
Yunqing Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Remote Sensing Image ◽  
Image Features ◽  
Remote Sensing Images ◽  
The Rich ◽  
The Difference ◽  
Basic Network ◽  
And Performance

Remote sensing image change detection (CD) is an important task in remote sensing image analysis and is essential for an accurate understanding of changes in the Earth’s surface. The technology of deep learning (DL) is becoming increasingly popular in solving CD tasks for remote sensing images. Most existing CD methods based on DL tend to use ordinary convolutional blocks to extract and compare remote sensing image features, which cannot fully extract the rich features of high-resolution (HR) remote sensing images. In addition, most of the existing methods lack robustness to pseudochange information processing. To overcome the above problems, in this article, we propose a new method, namely MRA-SNet, for CD in remote sensing images. Utilizing the UNet network as the basic network, the method uses the Siamese network to extract the features of bitemporal images in the encoder separately and perform the difference connection to better generate difference maps. Meanwhile, we replace the ordinary convolution blocks with Multi-Res blocks to extract spatial and spectral features of different scales in remote sensing images. Residual connections are used to extract additional detailed features. To better highlight the change region features and suppress the irrelevant region features, we introduced the Attention Gates module before the skip connection between the encoder and the decoder. Experimental results on a public dataset of remote sensing image CD show that our proposed method outperforms other state-of-the-art (SOTA) CD methods in terms of evaluation metrics and performance.

A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images

2020 ◽  
Vol 166 ◽  
pp. 183-200 ◽  
Author(s):  
Chenxiao Zhang ◽  
Peng Yue ◽  
Deodato Tapete ◽  
Liangcun Jiang ◽  
Boyi Shangguan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Image Fusion ◽  
Remote Sensing Images
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