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CVMFusion: Fusión de ConvNeXtV2 y Visual Mamba para Segmentación en Teledetección

Zelin Wang1, Li Qin2, Cheng Xu1

  • 1National Key Laboratory of Complex System Control and Intelligent Agent Cooperation, Beijing 100074, China.

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CVMFusion mejora la extracción de líneas de costa de imágenes de teledetección combinando CNNs y Mamba para un análisis local y global detallado. Este novedoso enfoque mejora la precisión de la segmentación tierra-mar, especialmente para límites complejos y objetos pequeños.

Palabras clave:
Mambamecanismos de atenciónred neuronal convolucionalsegmentación de imágenesteledetección

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Área de la Ciencia:

  • Teledetección
  • Visión por Computadora
  • Sistemas de Información Geográfica

Sus antecedentes:

  • La extracción de líneas de costa de imágenes de teledetección de alta resolución presenta desafíos debido a detalles complejos y variabilidad del objetivo.
  • Las Redes Neuronales Convolucionales (CNNs) existentes tienen dificultades con las dependencias a largo plazo, mientras que los Transformers son computacionalmente costosos.
  • Existe la necesidad de métodos de segmentación tierra-mar eficientes y precisos que puedan manejar detalles intrincados y contexto global.

Objetivo del estudio:

  • Proponer CVMFusion, una novedosa red de segmentación tierra-mar diseñada para superar las limitaciones de los métodos actuales.
  • Integrar las fortalezas de las CNNs para la extracción de características locales y Mamba para la modelización del contexto global.
  • Lograr una alta precisión en la extracción de líneas de costa, particularmente en escenarios desafiantes que involucran objetos pequeños y límites complejos.

Principales métodos:

  • Se desarrolló CVMFusion, una red codificador-decodificador en forma de U con organización jerárquica.
  • Se emplearon ramas paralelas de ConvNeXtV2 y VMamba en el codificador para la captura de características locales y globales.
  • Se integraron módulos de Atención Dinámica Multi-Escala (DyMSA) y Atención Cruzada Dinámica Ponderada (DyWCA) para la fusión adaptativa de características a través de conexiones de salto.

Principales resultados:

  • CVMFusion logró precisiones de Intersección sobre Unión (MIoU) del 98,05 % y 96,28 % en dos conjuntos de datos públicos.
  • La red propuesta superó a los métodos existentes de segmentación tierra-mar.
  • Demostró un rendimiento superior en la segmentación de objetos pequeños y regiones de límites intrincados.

Conclusiones:

  • CVMFusion fusiona eficazmente características locales y globales para una extracción precisa de líneas de costa.
  • Los mecanismos de fusión adaptativa y la estructura jerárquica contribuyen a mejorar el rendimiento de la segmentación.
  • CVMFusion representa un avance significativo en la segmentación tierra-mar para imágenes de teledetección de alta resolución.