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Local and Long-Range Collaborative Learning for Remote Sensing Scene Classification
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AI
DOI：10.1109/TGRS.2023.3265346.png)
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摘要
En 中文
With the development of high-resolution satellites, more and more attention has been paid to remote sensing (RS) scene classification. Convolutional neural networks (CNNs), which replace the traditional handcrafted features with a learning-based feature extraction mechanism, are widely used in scene classification. But, CNNs are less effective in deriving long-range contextual relations, which limits further improvement. Visual transformer (VT), an emerging image processing method, provides a new perspective for RS scene classification by directly acquiring long-range features. Although there have been limited works combining CNN and VT through simple concatenation, the collaborations between them are insufficient. To address these issues, we propose a local and long-range collaborative framework (L2RCF). First, we design a dual-stream structure to extract the local and long-range features. Second, a cross-feature calibration (CFC) module is designed for them to improve the representation of the fusion features. Then, combining deep supervision (DS) and deep mutual learning (DML), a novel joint loss is proposed to enhance the dual-stream feature extractor and further improve the fused features. Finally, a two-stage semi-supervised training strategy is designed to improve performance with unlabeled samples. To demonstrate the effectiveness of L2RCF, we conducted experiments on three widely used RS scene classification datasets: RSSCN7, AID, and NWPU. The results show that the L2RCF performs significantly better compared with some state-of-the-art scene classification methods.
Keyword:
Feature extraction
Transformers
Training
Task analysis
Visualization
Convolutional neural networks
Remote sensing
Convolutional neural network (CNN)
cross-feature calibration (CFC)
deep mutual learning (DML)
deep supervision (DS)
remote sensing (RS)
scene classification
semi-supervised
visual transformer (VT)
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
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