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Remote Sensing Scene Classification Based on Attention-Enabled Progressively Searching
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AI
DOI：10.1109/TGRS.2022.3186588.png)
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Abstract
En 中文
Remote sensing image scene classification plays a significant role in remote sensing image analysis. Aiming at the problems of large transformation and scale variation of background and key objects in remote sensing images, we propose a neural architecture search (NAS) method based on attention search space. The network adaptively searches convolution, pooling, and attention operations in the appropriate layers. To ensure the stability of the searching process, a multistage network progressive fusion search method is proposed, which discards useless operations in stages, reduces the burden of search algorithm, and improves the search efficiency. Finally, paying attention to the association information between objects and scenes, a bottom-up multiscale fusion network connection strategy is proposed to fully reuse the semantics of multiscale feature maps in each stage. The experimental results show that the proposed method performs better than the manual method and the current neural network architecture search method.
Keywords:
Computer architecture
Remote sensing
Image analysis
Feature extraction
Task analysis
Semantics
Microprocessors
Convolutional neural network (CNN)
feature fusion
neural architecture search (NAS)
remote sensing scene classification
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W