Open AccessAdaptive Filtering Remote Sensing Image Segmentation Network based on Attention Mechanism
Author(s) -
Cong Wu,
Hao Dong,
Xuan Lin,
Hanqiao Jiang,
Li quan Wang,
Xin zhi Liu,
Wei Shi
Publication year - 2021
Publication title -
computer science and information technology ( cs and it )
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2021.110903
Subject(s) - computer science , artificial intelligence , upsampling , segmentation , image segmentation , pattern recognition (psychology) , computer vision , convolutional neural network , feature extraction , feature (linguistics) , image (mathematics) , linguistics , philosophy
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.