Block Compressed Sensing of Multispectral Remote Sensing Image by Adaptive Filtering Prediction

2013 ◽  
Vol 5 (6) ◽  
pp. 22-30 ◽  
Author(s):  
Xuan Wang ◽  
Wenxuan Shi ◽  
Dexiang Deng
Keyword(s):  
Remote Sensing ◽  
Compressed Sensing ◽  
Adaptive Filtering ◽  
Remote Sensing Image ◽  
Block Compressed Sensing ◽  
Multispectral Remote Sensing

Remote sensing images fusion based on block compressed sensing

2013 ◽  
Author(s):  
Sen-lin Yang ◽  
Guo-bin Wan ◽  
Bian-lian Zhang ◽  
Xin Chong
Keyword(s):  
Remote Sensing ◽  
Compressed Sensing ◽  
Remote Sensing Images ◽  
Block Compressed Sensing

scholarly journals Adaptive Filtering Remote Sensing Image Segmentation Network based on Attention Mechanism

2021 ◽  
Author(s):  
Cong zhong Wu ◽  
Hao Dong ◽  
Xuan jie Lin ◽  
Han tong Jiang ◽  
Li quan Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Adaptive Filtering ◽  
High Frequency ◽  
Remote Sensing Image ◽  
Aerial Image ◽  
Data Sets ◽  
Road Extraction ◽  
Image Labeling ◽  
Dilated Convolution ◽  
Segmentation Accuracy

It is difficult to segment small objects and the edge of the object because of larger-scale variation, larger intra-class variance of background and foreground-background imbalance in the remote sensing imagery. In convolutional neural networks, high frequency signals may degenerate into completely different ones after downsampling. We define this phenomenon as aliasing. Meanwhile, although dilated convolution can expand the receptive field of feature map, a much more complex background can cause serious alarms. To alleviate the above problems, we propose an attention-based mechanism adaptive filtered segmentation network. Experimental results on the Deepglobe Road Extraction dataset and Inria Aerial Image Labeling dataset showed that our method can effectively improve the segmentation accuracy. The F1 value on the two data sets reached 82.67% and 85.71% respectively.

Sign in / Sign up

Export Citation Format

Share Document