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MAFMamba: Una Red de Fusión Adaptativa Multiescala para la Segmentación Semántica de Imágenes de Teledetección de

Boxu Li¹, Xiaobing Yang¹, Yingjie Fan¹

¹College of Information Engineering, China Jiliang University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)

|January 28, 2026

Resumen

MAFMamba, una novedosa red visual Mamba, mejora la segmentación semántica para imágenes de teledetección de alta resolución al manejar eficazmente las variaciones de escala y equilibrar los detalles locales con el contexto global.

Área de la Ciencia:

Visión por Computadora
Teledetección
Inteligencia Artificial

Sus antecedentes:

La imagen de teledetección de alta resolución es crucial para la adquisición de información geoespacial.
Los modelos actuales de segmentación semántica luchan con las variaciones de escala y la preservación de detalles finos.
Los desafíos incluyen límites borrosos, pérdida de objetos pequeños y modelado de diversas apariencias de objetos.

Objetivo del estudio:

Presentar MAFMamba, una red Mamba visual de fusión adaptativa multiescala para la segmentación de imágenes de teledetección de alta resolución.
Abordar las limitaciones en la captura del contexto global y los detalles locales, y el modelado de variaciones de escala extremas.
Mejorar la precisión y la eficiencia de la segmentación semántica en escenas complejas de teledetección.

Principales métodos:

Se desarrolló un codificador híbrido ligero con un Bloque Mamba Adaptativo Multiescala (AMMB) y Fusión Adaptativa Multiescala (MSAF) para una representación multiescala robusta.
Se introdujo un Mamba de Mejora de Características Global-Local (GLMamba) en el decodificador para equilibrar las características locales convolucionales con las dependencias globales de VSS.
Se propuso un módulo de Fusión de Atención Cruzada Multiescala (MSCAF) para cerrar las brechas semánticas entre las características del codificador y el decodificador.

Principales resultados:

MAFMamba superó a los métodos de CNN, Transformer y Mamba de última generación en los conjuntos de datos ISPRS Potsdam y Vaihingen en las puntuaciones mIoU y mF1.
La red logró una precisión superior en la segmentación semántica de imágenes de teledetección de alta resolución.
Demostró complejidad computacional lineal y bajo uso de memoria, lo que indica una alta eficiencia.

Conclusiones:

MAFMamba aborda eficazmente la variación de escala y el equilibrio entre los detalles locales y la semántica global en la segmentación de imágenes de teledetección.
La arquitectura propuesta ofrece un avance significativo para la adquisición precisa y eficiente de información geoespacial.
MAFMamba presenta una solución prometedora para escenarios complejos de teledetección que requieren una delimitación precisa de objetos.

Palabras clave:

Mamba visual imágenes de teledetección de alta resolución fusión multiescala segmentación semántica

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