Axial Cross Attention Meets CNN: Bibranch Fusion Network for Change Detection

计算机科学 人工智能 增采样 卷积神经网络 特征提取 保险丝(电气) 模式识别(心理学) 计算机视觉 像素 图像(数学) 电气工程 工程类
作者
Lei Song,Min Xia,Liguo Weng,Haifeng Lin,Ming Qian,Binyu Chen
出处
期刊:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:16: 21-32 被引量:53
标识
DOI:10.1109/jstars.2022.3224081
摘要

In the previous years, vision transformer has demonstrated a global information extraction capability in the field of computer vision that convolutional neural network (CNN) lacks. Due to the lack of inductive bias in vision transformer, it requires a large amount of data to support its training. In the field of remote sensing, it costs a lot to obtain a significant number of high-resolution remote sensing images. Most existing change detection networks based on deep learning rely heavily on the CNN, which cannot effectively utilize the long-distance dependence between pixels for difference discrimination. Therefore, this work aims to use a high-performance vision transformer to conduct change detection research with limited data. A bibranch fusion network based on axial cross attention (ACABFNet) is proposed. The network extracts local and global information of images through the CNN branch and transformer branch, respectively, and then, fuses local and global features by the bidirectional fusion approach. In the upsampling stage, similar feature information and difference feature information of the two branches are explicitly generated by feature addition and feature subtraction. Considering that the self-attention mechanism is not efficient enough for global attention over small datasets, we propose the axial cross attention. First, global attention along the height and width dimensions of images is performed respectively, and then cross attention is used to fuse the global feature information along two dimensions. Compared with the original self-attention, the structure is more graphics processing unit friendly and efficient. Experimental results on three datasets reveal that the ACABFNet outperforms existing change detection algorithms.
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