Mamba-Enhanced Spatial–Spectral Feature Learning for Hyperspectral Unmixing

Convolutional neural networks (CNNs) and transformers have demonstrated remarkable capabilities in capturing spatial–spectral contextual dependencies, making them widely applicable for hyperspectral unmixing tasks. However, constrained by limited receptive fields and high computational comple...

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Dades bibliogràfiques

Autors principals:	Mingle Zhang, Hongyu Xie, Mingyu Yang, Qingbin Jiao, Liang Xu, Xin Tan
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2025-01-01
Col·lecció:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Matèries:	Convolutional neural network (CNN) deep learning hyperspectral unmixing (HU) Mamba state-space models (SSMs)
Accés en línia:	https://ieeexplore.ieee.org/document/11125314/
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