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Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

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循环精制的多决策联合对齐网络,用于无监督域适应性超频谱变化检测.

Jiahui Qu, Wenqian Dong, Yufei Yang

IEEE transactions on neural networks and learning systems

|January 3, 2024

概括

这项研究引入了一种用于无监督域自适应超谱变化检测的新方法. 经过循环改进的多决策联合对齐网络 (CMJAN) 有效地减少了域移动,以改善土地覆盖变化分析.

科学领域:

遥感遥感遥感遥感
地理空间分析的研究.
机器学习机器学习

背景情况:

超光谱变化检测对于监测地球陆地覆盖面至关重要.
深度学习方法优秀,但需要昂贵的标记数据.
数据集之间的域名转移显著降低了性能.

研究的目的:

开发一种无监督域适应方法,用于超谱变化检测.
为了应对遥感领域领域转移的挑战.
为了提高土地覆盖变化分析在新的,未标记的场景的准确性.

主要方法:

建议建立一个循环完善的多决策联合调整网络 (CMJAN).
在源域和目标域之间逐步调整数据分布.
使用循环精制的高可靠性标记样本进行适应.

主要成果:

在CMJAN逐步缓解分配差异.
学习域不变差异特征表示.
在各种数据集上,与最先进的方法相比,表现出更高的性能.

结论:

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拟议的CMJAN有效地减轻了无监督超频谱变化检测中的域转移.

能够在没有标记数据的目标领域准确地分析土地覆盖面变化.

为实际的遥感应用提供了有前途的解决方案.