Publication: Semi-Supervised Semantic Segmentation of Remote Sensing Images Based on Dual Cross-Entropy Consistency
Semi-Supervised Semantic Segmentation of Remote Sensing Images Based on Dual Cross-Entropy Consistency
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Cui, M., Li, K., Li, Y., Kamuhanda, D., & Tessone, C. (2023). Semi-Supervised Semantic Segmentation of Remote Sensing Images Based on Dual Cross-Entropy Consistency. Entropy, 25(4), 681. https://doi.org/10.3390/e25040681
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Semantic segmentation is a growing topic in high-resolution remote sensing image processing. The information in remote sensing images is complex, and the effectiveness of most remote sensing image semantic segmentation methods depends on the number of labels; however, labeling images requires significant time and labor costs. To solve these problems, we propose a semi-supervised semantic segmentation method based on dual cross-entropy consistency and a teacher–student structure. First, we add a channel attention mechanism to the
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- Cui, Mengtian
- Li, Kai
- Li, Yulan
- Kamuhanda, Dany
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Cui, M., Li, K., Li, Y., Kamuhanda, D., & Tessone, C. (2023). Semi-Supervised Semantic Segmentation of Remote Sensing Images Based on Dual Cross-Entropy Consistency. Entropy, 25(4), 681. https://doi.org/10.3390/e25040681
