Title :

DNGAE: Deep Neighborhood Graph Autoencoder for Robust Blind Hyperspectral Unmixing

Author(s) :

Hanachi, Refka [Auteur]
Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] [RIADI]
Sellami, Akrem [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Farah, Imed Riadh [Auteur]
Département lmage et Traitement Information [IMT Atlantique - ITI]
Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] [RIADI]
Dalla Mura, Mauro [Auteur]
GIPSA - Signal Images Physique [GIPSA-SIGMAPHY]

Conference title :

ICCCI 2023- 15th International Conference on Computational Collective Intelligence (ICCCI)

City :

Budapest

Country :

Hongrie

Start date of the conference :

2023-09-27

Book title :

International Conference on Computational Collective Intelligence

Journal title :

Lecture Notes in Computer Science

Publisher :

Springer Nature Switzerland

Publication place :

Cham

Publication date :

2023-09-13

English keyword(s) :

Spectral unmixing
Neighborhood graph
Representation learning
Graph Autoencoder

HAL domain(s) :

Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]

English abstract : [en]

Recently, Deep Learning (DL)-based unmixing techniques have gained popularity owing to the robust learning of Deep Neural Networks (DNNs). In particular, the Autoencoder (AE) model, as a baseline network for unmixing, ...
Show more >Recently, Deep Learning (DL)-based unmixing techniques have gained popularity owing to the robust learning of Deep Neural Networks (DNNs). In particular, the Autoencoder (AE) model, as a baseline network for unmixing, performs well in Hyperspectral Unmixing (HU) by automatically learning a new representation and recovering original data. However, patch-wise AE based architecture, which incorporates both spectral and spatial information through convolutional filters may blur the abundance maps due to the fixed kernel shape of the used window size. To cope with the above issue, we propose in this paper a novel methodology based on graph DL called DNGAE. Unlike the pixel-wise or patch-wise Convolutional AE (CAE), our proposed method incorporates the complementary spatial information based on graph spectral similarity. A neighborhood graph based on band correlations is firstly constructed. Then, our method attempts to aggregate similar spectra from the neighboring pixels of a target pixel. Consequently, this leads to better quality of both extracted endmembers and abundances. Extensive experiments performed on two real HSI benchmarks confirm the effectiveness of our proposed method compared to other DL models.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL