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超光谱异常检测利用空间注意力和右移光谱能量

Ruhan A¹, Quanxue Gao², Xiaoni Zhang³

¹Xianyang Normal University, Xianyang, Shaanxi, China.

|September 4, 2025

概括

我们开发了一种新的图形注意网络-贝塔波形图形神经网络 (GAN-BWGNN) 用于高光谱异常检测. 这种方法通过整合空间和光谱数据准确地识别异常,以更快的处理时间超过现有技术.

科学领域:

遥感技术
计算机视觉
信号处理

背景情况:

超光谱成像 (HSI) 产生了丰富的光谱信息,对于详细分析至关重要.
在HSI中检测异常对于识别罕见目标或异常特征至关重要.
现有的方法往往难以有效和高效地整合空间和光谱信息.

研究的目的:

提出一种新的超光谱异常检测算法,GAN-BWGNN HAD.
通过使用图形神经网络整合空间和光谱信息来提高异常检测的精度.
提高超光谱异常检测中的处理效率和防噪强度.

主要方法:

图表注意力网络-贝塔波段图表神经网络 (GAN-BWGNN) 方法.
使用K-最近邻居 (KNN) 进行空间相关的像素智能图形构造.
适应性注意力机制 (GAN) 用于空间特征优先级.
对于光谱异常检测和高效处理.

主要成果:

在六个真实世界和一个模拟的HSI数据集上表现出卓越的性能,由高的曲线下面面积 (AUC) 值证明.
达到了高达0. 999的AUC值,明显超过了最新的方法.
证明了次秒检测时间 (0.20-0.28秒),比传统和深度学习模型提供了实质性的加快速度.

结论:

拟议的GAN-BWGNN HAD方法为超光谱异常检测提供了一个高度准确和高效的解决方案.
图形神经网络和β波小组的集成有效地利用了空间和光谱信息.
这种新方法代表了高光谱数据分析和目标检测的重大进步.

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