对基于多光谱和多时间图像的灾难映射进行概率子空间的无监督学习
Azubuike Okorie1, Chandra Kambhamettu2, Sokratis Makrogiannnis1
1Division of Physics, Engineering, Mathematics, and Computer Sciences, Delaware State University, 1200 N. DuPont Hwy, Dover, DE 19901, USA.
Machine vision and applications
|April 8, 2024
概括
这项研究介绍了一种无监督的子空间学习方法,使用卫星图像来检测自然灾害损害. 该方法准确地识别了受损地区,有助于灾难应对和评估.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理空间分析是什么
- 灾害管理 灾害管理
背景情况:
- 准确识别自然灾害损害对于有效应对和尽量减少生命损失至关重要.
- 卫星图像和遥感数据的进步使复杂的灾害监测算法成为可能.
研究的目的:
- 开发一种无监督的子空间学习方法来识别自然灾害损坏的地区,使用多时间和多光谱卫星图像.
- 评估该方法在各种灾害类型中的适用性,包括野火,洪水和地震/海事件.
主要方法:
- 该方法涉及区域划分,匹配和融合.
- 在共同的区域空间中应用无监督子空间学习来生成变化地图.
- 概率次空间距离被用来识别受损区域,并过非灾难变化.
主要成果:
- 该方法实现了森林火灾的平均子相似系数 (DSC) 为0.833,洪水的平均子相似系数为0.736.
- 在地震/海事件中获得的总体DSC为0.855.
- 与地面真相数据的验证证实了该方法的准确性.
结论:
- 开发的无监督子空间学习方法有效地识别了多种自然灾害类型中受损的区域.
- 高的DSC值表明强大的性能和适用于现实世界的灾难评估.
- 这种技术为及时准确地绘制灾后损害图提供了有价值的工具.
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