Deep Half-Siamese Networks for Hyperspectral Unmixing - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Geoscience and Remote Sensing Letters Année : 2021

Deep Half-Siamese Networks for Hyperspectral Unmixing

Zhu Han
Danfeng Hong
Lianru Gao
Bing Zhang

Résumé

Over the past decades, numerous methods have been proposed to solve the linear or nonlinear mixing problems in hyperspectral unmixing (HU). The existence of spectral variabilities and nonlinearity limits, to a great extent, the unmixing ability of most traditional approaches, particularly in complex scenes. In recent years, deep learning (DL) has been garnering increasing attention in nonlinear HU owing to its powerful learning and fitting ability. However, the DL-based methods tend to generate trivial unmixing results due to the lack of considering physically meaningful endmember information. To this end, we propose a novel siamese network, called the deep half-siamese network (Deep HSNet), for HU by fully considering diverse endmember properties extracted using different endmember extraction algorithms. Moreover, the proposed Deep HSNet, beyond the previous autoencoder-like architecture, adopts another subnetwork to learn the endmember information effectively to guide the unmixing process in a reasonable and accurate way. The experimental results conducted on the synthetic and real hyperspectral data sets validate the effectiveness and superiority of the Deep HSNet over several state-of-the-art unmixing algorithms.
Fichier non déposé

Dates et versions

hal-03429669 , version 1 (15-11-2021)

Identifiants

Citer

Zhu Han, Danfeng Hong, Lianru Gao, Bing Zhang, Jocelyn Chanussot. Deep Half-Siamese Networks for Hyperspectral Unmixing. IEEE Geoscience and Remote Sensing Letters, 2021, 18 (11), pp.1996-2000. ⟨10.1109/LGRS.2020.3011941⟩. ⟨hal-03429669⟩
31 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More