使用深度学习对秘鲁高山遥感图像的水体细分进行探索性分析
William Isaac Perez-Torres1, Diego Armando Uman-Flores1, Andres Benjamin Quispe-Quispe1
1LIECAR Laboratory, Universidad Nacional de San Antonio Abad del Cusco (UNSAAC), Cusco 08003, Peru.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
这项研究比较了用于监测秘鲁安第斯山脉高山湖泊的深度学习模型. WatNet和DeepWaterMapV2显示了类似的性能,WatNet在湖泊细分方面具有更高的计算效率.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 高山湖是重要的淡水资源和气候变化的指标.
- 监测这些湖泊对于了解环境动态至关重要.
- 高山地区的遥感因地形和大气而面临独特的挑战.
研究的目的:
- 在秘鲁安第斯山脉探索和比较湖泊监测的遥感技术.
- 评估三种深度学习模型 (DeepWaterMapV2,WatNet,WaterSegDiff) 对湖泊细分的性能.
- 评估这些模型对于高山湖泊监测的适用性.
主要方法:
- 为安卡什和库斯科地区创建了Landsat-8图像 (2013-2023) 的新数据集.
- 我们比较了三种深度学习模型 (DeepWaterMapV2,WatNet,WaterSegDiff) 和使用Otsu值的规范差异水指数 (NDWI).
- 使用了定量指标 (MIoU,PA,F1评分) 和定性分析.
主要成果:
- 在具有挑战性的条件下,DeepWaterMapV2和WatNet表现出相当的,足够的湖泊细分性能.
- WaterSegDiff显示了湖泊细分的有希望的质量结果.
- 具有较低计算复杂性的WatNet被确定为实施的相关模型.
结论:
- 深度学习模型,特别是WatNet,对于安第斯山脉高山湖泊监测是有效的.
- 在这些环境中,WatNet为湖泊细分提供了计算效率高的解决方案.
- 进一步的时间分析强调了WatNet在监测特定湖泊 (如Singrenacocha) 的显著行为.
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