S4DL:对转移敏感的空间光谱解学习,用于高光谱图像无监督域调整
IEEE transactions on neural networks and learning systems
|April 16, 2025
概括
这项研究引入了一种新的无监督域适应方法,用于高光谱图像分类. 拟议的方法通过解脱空间光谱信息,有效地处理域移动,改善跨场景分类性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 遥感 遥感 遥感 遥感
背景情况:
- 无监督域适应 (UDA) 对于高光谱图像 (HSI) 分类至关重要,利用标记的源数据和未标记的目标数据.
- 高质量分析提供了丰富的光谱信息,但存在重大领域转移挑战,往往降低了现有的UDA方法的性能.
- 当前的UDA技术往往忽略了光谱域信息,仅专注于特征分布对齐.
研究的目的:
- 提出一种新的无监督域适应方法,即移位敏感空间光谱解学习 (S4DL),用于高光谱图像分类.
- 解决现有方法在处理显著的光谱域转移和跨数据集的不同域转移方面的局限性.
- 通过学习对光谱变化敏感的域不变特征来提高跨场景HSI分类的稳定性和准确性.
主要方法:
- 开发了梯度引导的空间光谱分解 (GSSD) 以使用域分类梯度来分离域特定和域不变表示.
- 引入了一个移位敏感的自适应监视器,根据域移位的大小动态调整解的强度.
- 利用可逆神经网络来保存语义和浅层光谱域信息.
主要成果:
- 与最先进的UDA方法相比,S4DL在多个跨场景的HSI数据集上表现出更高的性能.
- 该方法显示了持续的改进,验证了其在处理重大域名转移方面的有效性.
- 实验结果证实了空间光谱信息解和适应域转移大小的优势.
结论:
- 拟议的S4DL方法有效地解决了HSI分类领域转移所带来的挑战.
- 通过智能地解并适应空间-光谱域变化,S4DL实现了最先进的结果.
- 该方法为跨场景的HSI分类提供了一个强大的解决方案,优于现有的UDA技术.
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