热带干森林土地使用/土地覆盖面变化检测使用半监督深度学习算法和遥感
Juan C González-Vélez1, Maria C Torres-Madronero2, Juan D Martínez-Vargas3
1Instituto Tecnologico Metropolitano (ITM), Calle 54a No. 30-01, Medellin, 050012, Colombia.
Environmental monitoring and assessment
|February 2, 2026
概括
这项研究引入了一种新的半监督深度学习框架,用于使用综合雷达和光学卫星数据绘制热带干森林 (TDF) 地图. 该方法实现了高精度,即使有有限的标记数据,也有助于保护工作.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 计算机科学 计算机科学
背景情况:
- 热带干旱森林 (TDFs) 是至关重要的,但由于光谱相似性和景观复杂性,使用遥感绘制地图具有挑战性.
- 数据稀缺的地区面临着传统的土地利用和土地覆盖 (LULC) 分类方法的局限性.
研究的目的:
- 开发一种新的半监督深度学习 (DL) 框架,以使用融合SAR和光学卫星图像进行准确的TDF变化检测.
- 为了应对在数据稀缺环境中与有限的标记数据对TDF进行映射的挑战.
主要方法:
- 开发了一个半监督的DL框架,结合了无监督的伪标签和自定义的Y-Net架构.
- 该框架融合了合成光圈雷达 (SAR) 和光学卫星图像,以增强变化检测.
- 该模型被评估其在检测TDF变化的性能与不同数量的标记数据.
主要成果:
- 拟议的框架实现了最先进的结果,平均整体准确率为95.3%,平均跨欧盟交叉点 (mIoU) 为88.1%.
- 半监督方法保持了90%以上的准确性,即使仅有60%的标记数据,也证明了它的稳定性.
- 该框架成功地绘制了2017年至2021年期间哥伦比亚考卡河谷的TDF变化.
结论:
- 新的半监督DL框架为TDF变化检测提供了强大的解决方案,特别是在数据有限的区域.
- 这种方法促进了环境监测,保护和可持续资源管理的遥感应用.
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