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Método de transferencia de aprendizaje para la extracción de deslizamientos de tierra después del terremoto de Wenchuan utilizando imágenes de alta resolución del primer satélite chino

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：lizhen18@aircas.ac.cn
  • Introduction：李震，研究方向为遥感图像处理。E-mail：lizhen18@aircas.ac.cn
LI Zhen12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：lishanshan@aircas.ac.cn
  • Introduction：李山山，研究方向为遥感图像处理。E-mail：lishanshan@aircas.ac.cn
LI Shanshan1*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

GE Xiaoqing1

resumen

El terremoto de magnitud 8,0 de Wenchuan en 2008 desencadenó una gran cantidad de deslizamientos de tierra y colapsos geológicos, lo que resultó en una alta frecuencia de desastres geológicos en la zona de alto nivel de intensidad sísmica después del terremoto, y debido a su gran amenaza para la vida y la propiedad, ha generado un amplio interés. La extracción rápida de información sobre deslizamientos de tierra mediante teledetección y otras tecnologías es de gran importancia para reducir las pérdidas causadas por los desastres. Este artículo propone un método de transferencia de aprendizaje, aprendiendo características de un conjunto de datos de escenas naturales, y transfiriéndolas a la extracción de deslizamientos de tierra. Este método primero se entrena en la red ResNet en ImageNet, luego ingresan muestras de imágenes de áreas de deslizamiento de tierra, y trasladan la red preentrenada y sus parámetros a LinkNet para finalmente realizar la extracción de los deslizamientos de tierra. El análisis y la validación de la experiencia de extracción de deslizamientos de tierra posteriores al terremoto de Wenchuan de 2013 a 2015 mostraron que el método de transferencia de aprendizaje propuesto en este artículo tiene una precisión de extracción superior en comparación con el método tradicional de máquina de vectores de soporte y otros métodos de aprendizaje profundo, lo que favorece la evaluación y la toma de decisiones subsiguientes.

palabra clave

Teledetección; Extracción de deslizamientos de tierra; Transferencia de aprendizaje; ImageNet; Primer satélite chino de alta resolución

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