渐进空间信息引导深度聚合卷积网络,用于高光谱的光谱超分辨率
IEEE transactions on neural networks and learning systems
|October 27, 2023
概括
这项研究介绍了SIGnet,SIGnet是一个新的深度学习网络,用于高光谱图像的光谱超分辨率. 通过更好地利用跨模式信息来改进图像重建,SIGnet 改进了基于融合的方法.
科学领域:
- 遥感 遥感 遥感 遥感
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 超光谱图像 (HSI) 光谱超分辨率 (SSR) 旨在通过深度学习来提高图像分辨率.
- 目前基于融合的方法难以有效利用跨模式信息,限制性能,特别是在更大的规模上.
研究的目的:
- 提出一个新的深度聚合卷积神经网络,SIGnet,用于改进HSI光谱超分辨率.
- 加强高分辨率多光谱图像 (HR-MSI) 和低分辨率超光谱图像 (LR-HSI) 的融合.
主要方法:
- 开发了SIGnet,一个具有密集的残留通道亲和学习 (DRCA) 块和空间引导传播 (SGP) 模块的网络.
- DRCA块使用道亲和传播 (CAP) 模块来建模特征地图道之间的相互依赖.
- 该SGP模块通过降解模拟和可变形自适应融合逐步改进HSI特征.
主要成果:
- 与现有的基于融合的最先进方法相比,SIGnet 显示出更高的性能.
- 拟议的方法实现了更好的重建质量,特别是在更大的升样规模.
结论:
- SIGnet有效地解决了当前基于聚变的HSI SSR方法的局限性.
- 该网络的架构增强了利用跨模式信息的优越光谱超分辨率.
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