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Article Dans Une Revue Knowledge-Based Systems Année : 2024

Graph feature fusion driven by deep autoencoder for advanced hyperspectral image unmixing

Résumé

In this paper, we propose a pioneering approach for blind Hyperspectral Image (HSI) unmixing named Multi-Features Graph Deep Fusion Learning Networks for HSI Unmixing (MF-GDL). Our method leverages the power of multi-feature graph deep fusion learning networks. MF-GDL integrates spectral HSI graph features with complementary spatial graph features, overcoming the fixed window size limitation observed in prior deep learning approaches in capturing spatial relationships effectively. A key contribution lies in the introduction of multi-view graph learning, a novel aspect in HSI unmixing. By incorporating graph similarity-based spatial information, we address an important gap in the existing literature, marking the first attempt, to our knowledge, to tackle this challenge using a graph deep learning method. Furthermore, we introduce auxiliary information based on Extended Morphological Profiles (EMPs) to enrich spatial context beyond high-level spatial relationships between neighboring pixels. This multi-view fusion learning not only improves accuracy in material abundance estimation but also demonstrates the significance of considering multiple perspectives in HSI unmixing. We validate the efficacy of MF-GDL on four real datasets: Jasper Ridge, Samson, Urban, and Apex showcasing its robust performance and highlighting the substantial contribution of the multi-view aspect in advancing HSI unmixing techniques.
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Dates et versions

hal-04613297 , version 1 (16-06-2024)

Identifiants

Citer

Refka Hanachi, Akrem Sellami, Imed Riadh Farah, Mauro Dalla Mura. Graph feature fusion driven by deep autoencoder for advanced hyperspectral image unmixing. Knowledge-Based Systems, inPress, 299, pp.112087. ⟨10.1016/j.knosys.2024.112087⟩. ⟨hal-04613297⟩
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