A Dual-Branch CNN with Depthwise Separable Fusion for Hyperspectral Image Classification.
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| Title: | A Dual-Branch CNN with Depthwise Separable Fusion for Hyperspectral Image Classification. |
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| Authors: | Li, Teng1 (AUTHOR), Cao, Yunhua1,2 (AUTHOR) yhcao@mail.xidian.edu.cn, Guo, Xing1,2 (AUTHOR), Zhang, Shikun1,2 (AUTHOR), Yan, Lining1 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1685. 32p. |
| Subjects: | Convolutional neural networks, Image recognition (Computer vision), Quantitative research |
| Abstract: | Highlights: What are the main findings? DSFA-CNN introduces a dual-branch framework to jointly preserve spatial–spectral coupling and learn complementary spectral and spatial features. CBAM-enhanced feature extraction and depthwise separable fusion improve classification performance while reducing feature redundancy. What are the implications of the main findings? The proposed method achieves a favorable balance between classification accuracy, interpretability, and computational efficiency. The framework provides an effective and well-balanced design for hyperspectral image classification in complex scenes. Hyperspectral image classification remains challenging because robust recognition requires preserving spatial–spectral coupling, extracting complementary spectral and spatial cues, and fusing heterogeneous features without excessive redundancy. To address this issue, a dual-branch convolutional neural network (CNN) with depthwise separable fusion, termed DSFA-CNN, is developed. The network combines a 3D convolution branch for coupled spatial–spectral representation learning with a 1D+2D branch for efficient spectral and spatial modeling. A convolutional block attention module (CBAM) is introduced in the decomposed branch to emphasize informative spectral responses and salient spatial regions, and a depthwise separable fusion module is used to improve cross-branch integration while limiting fusion-stage redundancy and the risk of overfitting. Experiments on Indian Pines, University of Pavia, Salinas, and Houston2013 yield overall accuracies of 95.62 ± 0.13%, 99.25 ± 0.13%, 99.89 ± 0.11%, and 97.62 ± 0.23%, respectively. The gains are most evident on the more challenging Indian Pines and Houston2013 scenes. Ablation results show that the dual-branch design provides complementary information, whereas CBAM and the fusion module further improve representation selectivity and feature integration. Computational cost analysis further indicates that DSFA-CNN achieves a more favorable trade-off between classification accuracy and computational efficiency than several recent competitive baselines. These results demonstrate the effectiveness of parallel coupled–decomposed modeling with efficient feature fusion for robust hyperspectral image classification. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? DSFA-CNN introduces a dual-branch framework to jointly preserve spatial–spectral coupling and learn complementary spectral and spatial features. CBAM-enhanced feature extraction and depthwise separable fusion improve classification performance while reducing feature redundancy. What are the implications of the main findings? The proposed method achieves a favorable balance between classification accuracy, interpretability, and computational efficiency. The framework provides an effective and well-balanced design for hyperspectral image classification in complex scenes. Hyperspectral image classification remains challenging because robust recognition requires preserving spatial–spectral coupling, extracting complementary spectral and spatial cues, and fusing heterogeneous features without excessive redundancy. To address this issue, a dual-branch convolutional neural network (CNN) with depthwise separable fusion, termed DSFA-CNN, is developed. The network combines a 3D convolution branch for coupled spatial–spectral representation learning with a 1D+2D branch for efficient spectral and spatial modeling. A convolutional block attention module (CBAM) is introduced in the decomposed branch to emphasize informative spectral responses and salient spatial regions, and a depthwise separable fusion module is used to improve cross-branch integration while limiting fusion-stage redundancy and the risk of overfitting. Experiments on Indian Pines, University of Pavia, Salinas, and Houston2013 yield overall accuracies of 95.62 ± 0.13%, 99.25 ± 0.13%, 99.89 ± 0.11%, and 97.62 ± 0.23%, respectively. The gains are most evident on the more challenging Indian Pines and Houston2013 scenes. Ablation results show that the dual-branch design provides complementary information, whereas CBAM and the fusion module further improve representation selectivity and feature integration. Computational cost analysis further indicates that DSFA-CNN achieves a more favorable trade-off between classification accuracy and computational efficiency than several recent competitive baselines. These results demonstrate the effectiveness of parallel coupled–decomposed modeling with efficient feature fusion for robust hyperspectral image classification. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111685 |
