Name
Abstract | ||
|---|---|---|
Remote sensing scene classification (RSSC) is a fundamental yet challenging task in the domain of remote sensing (RS). Currently, the methods based on deep features from convolutional neural networks (CNNs) have significantly improved the scene classification accuracy (ACC). However, the standard convolution operations have limited capacity to model the long-range correlations and cannot effectively obtain global contextual understanding ability. In this letter, we propose a novel scene classification framework, termed cross-layer nonlocal network (CL-NL-Net), consisting of a backbone network, a cross-layer nonlocal (CL-NL) module, and a classifier. Among them, the backbone network is used to obtain multilayer convolutional features. The CL-NL module is the core of the proposed method, which captures the long-range correlations between different layers, so as to achieve a better global scene understanding ability. To verify the effectiveness of the proposed CL-NL-Net, we conduct experiments on four benchmark datasets, and the results demonstrate that the proposed method achieves competitive classification ACC and outperforms some state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3078468 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Standards, Correlation, Feature extraction, Convolution, Task analysis, Remote sensing, Computer architecture, Convolutional neural network (CNN), cross-layer nonlocal network (CL-NL-Net), remote sensing scene classification (RSSC) | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 5 | |