S3-Net:学习光谱空间自我相似性,以获得超光谱图像超分辨率
Xinying Wang1, Zhixiong Huang1, Jiawen Zhu2
1School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
概括
这项研究介绍了S3-Net,这是一种超高分辨率的超光谱图像 (HSI) 的新框架. 它有效地利用光谱空间自我相似性,在重建高分辨率HSI数据方面超过现有方法.
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:
- 超分辨率的高光谱图像 (HSI) 对于详细分析至关重要.
- 将HSI与多光谱图像 (MSI) 融合提供了一个经济上可行的方法.
- 现有的方法在HSI中未充分利用自我相似性,只关注空间维度.
研究的目的:
- 提出一个新的框架,S3-Net,用于在HSI中跨光谱空间维度的全面自我相似性先行学习.
- 通过探索跨带自我相似性和光谱空间相关性来增强高光谱图像超分辨率.
- 开发一个模型,克服当前HSI超分辨率技术的次优解决方案.
主要方法:
- 开发了S3-Net,这是一个利用Tribranch自我相似性融合 (TSSF) 块的框架.
- 在TSSF块内内置非局部自我相似性注意 (NLSSA) 模块,以实现高效的特征聚合.
- 在TSSF块内采用排列操作,以方便多维光谱空间分析.
主要成果:
- 在超分辨率的高光谱图像中,S3-Net表现出卓越的性能.
- 在休斯顿,帕维亚中心,城市和雄安数据集上的实验验证了该模型的有效性.
- 该框架成功地捕获了复杂的光谱空间相关性和跨频段的自我相似性.
结论:
- S3-Net为HSI超分辨率的自我相似性先前学习提供了更全面的方法.
- 提出的方法有效地通过利用光谱空间相关性来重建高分辨率的HSI.
- 该框架在HSI超分辨率技术方面取得了重大进展.
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