سابِق|تال

CBAM UNet+++： Attention mechanism to guide change detection studies of full-scale connected networks

  • role： First author第一作者
  • Affiliation：

    School of Surveying and Mapping Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China

  • Email：liuying712100@163.com
  • Introduction：刘英，研究方向为环境遥感。E-mail：liuying712100@163.com
LIU Ying1,  
  • Affiliation：

    School of Surveying and Mapping Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China

HE Xue1,  
  • Affiliation：

    National Geographic Information System Engineering Technology Research Center, China University of Geosciences, Wuhan 430000, China

LI Danyang2,  
  • Affiliation：

    School of Surveying and Mapping Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China

YUE Hui1,  
  • Affiliation：

    School of Surveying and Mapping Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China

WEI Jiali1

ملخص

Existing change detection networks rely heavily on layer-by-layer convolution for feature extraction. However, the use of this method leads to a loss of information, and it lacks the ability to mine important change features. Therefore, knowing how to effectively suppress the influence of the background and identifying ways to increase the ability of the network to learn salient features and generate recognizable feature information are highly important for change detection tasks. Traditional skip connections lack the ability to obtain change information from a full-scale perspective and perform encoder feature extraction. Thus, a UNet+++ high-resolution remote sensing image change detection network called CBAM UNet+++ combined with a coupled attention mechanism (i.e., a convolutional block attention module [CBAM]) was designed in this research.CBAM UNet+++ is based on the semantic segmentation structure UNet+++. The unique full-scale concatenation operation of UNet+++ effectively fuses the semantic and spatial information from the full-scale perspective to avoid information loss. The basic convolutional unit can be replaced by a residual attention module (Residual Block_CBAM and ResBlock_CBAM) to suppress background effects and enhance the learning ability of the encoder to handle significant features. The residual attention module was validated on two remote sensing image change detection datasets—LEBEDV and LEVIR-CD—involving different high-resolution change regions.The proposed method has the highest accuracy on the LEBEDEV multifeature change dataset, with F1 and OA values of 88.9% and 97.3%, respectively, and the second highest accuracy on the LEVIR-CD building change dataset, with F1 and OA values of 86.7% and 96.8%, respectively. The proposed method can obtain deep semantics in a targeted manner, and its qualitative results are better than those of other benchmark networks.The CBAM UNet+++ method can accurately locate and detect change regions with better detection and accuracy than can the benchmark method. The accuracy results of the two selected datasets were slightly different, but they were not inconsistent. The accuracy of the CBAM UNet+++ model was disrupted by pseudochange information in the building dataset. Future work may focus on the usability of this network for change detection in heterogeneous dual-temporal images to further address the impact of early fusion on change detection accuracy.

مفهوم

remote sensing;change detection;UNet+++;attention mechanism;encoding and decoding

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