贪的合奏 超光谱异常检测检测
Mazharul Hossain1, Mohammed Younis2, Aaron Robinson2
1Computer Science Department, The University of Memphis, Memphis, TN 38152, USA.
Journal of imaging
|June 26, 2024
概括
一种新的贪集异常检测 (GE-AD) 方法自动选择最佳的超谱异常检测 (HS-AD) 算法. 这种方法显著提高了跨不同数据集的异常检测性能,优于单个和现有的合并方法.
科学领域:
- 计算机视觉 计算机视觉
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 超光谱图像提供了丰富的光谱信息,对计算机视觉任务有价值.
- 在超光谱图像中检测异常对于识别变化和异常至关重要.
- 现有的超谱异常检测 (HS-AD) 算法由于背景建模假设的多样性而存在局限性.
研究的目的:
- 开发一种自动化方法来选择最佳的HS-AD算法.
- 在各种场景中解决单个HS-AD算法的局限性.
- 为了提高超谱数据中异常检测的准确性和可靠性.
主要方法:
- 开发了贪集团异常检测 (GE-AD),一种两阶段的堆叠集团方法.
- 利用一个贪的搜索算法从HS-AD和高光谱解混算法中选择合适的基准模型.
- 在组件的第二阶段使用监督分类器进行最终异常检测.
主要成果:
- 与个人和最先进的合奏方法相比,GE-AD实现了统计学上显著的更高的平均F1宏分数.
- 在多个基准数据集上表现出卓越的性能,包括ABU,圣地亚哥,萨利纳斯,海迪斯城市和亚利桑那州.
- 在机场场景中,GE-AD表现出了显著的改进,在机场场景中高达28.53%的表现优于以前的方法.
结论:
- 贪搜索和堆叠组合的组合为自动化HS-AD模型选择提供了一个有效的策略.
- GE-AD提高了异常检测的准确性,并为具有有限的算法特定知识的研究人员提供了强大的解决方案.
- 这项工作有助于推进超光谱异常检测及其实际应用.
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