A Model-Driven Deep Mixture Network for Robust Hyperspectral Anomaly Detection

Li, Y; Jiang, K; Xie, W; Lei, J; Zhang, X; Du, Q

A Model-Driven Deep Mixture Network for Robust Hyperspectral Anomaly Detection

Li, Y Jiang, K Xie, W Lei, J

Zhang, X Du, Q

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Geoscience and Remote Sensing, 2023, 61
Issue Date:: 2023-01-01

Closed Access

	Filename	Description	Size
	A_Model-Driven_Deep_Mixture_Network_for_Robust_Hyperspectral_Anomaly_Detection.pdf	Published version	4.38 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Li, Y
dc.contributor.author	Jiang, K
dc.contributor.author	Xie, W
dc.contributor.author	Lei, J https://orcid.org/0000-0003-0851-6565
dc.contributor.author	Zhang, X
dc.contributor.author	Du, Q
dc.date.accessioned	2024-04-29T08:58:56Z
dc.date.available	2024-04-29T08:58:56Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Geoscience and Remote Sensing, 2023, 61
dc.identifier.issn	0196-2892
dc.identifier.issn	1558-0644
dc.identifier.uri	http://hdl.handle.net/10453/178475
dc.description.abstract	Hyperspectral anomaly detection (HAD) aims to identify samples with unknown atypical spectra from the background. Deep learning (DL)-based methods, particularly autoencoders (AEs), have proven effective in uncovering the underlying profiles for HAD. However, in real-world applications of hyperspectral images (HSIs), complex background land covers and anomaly corruptions are common, leading to two issues: 1) a low-dimensional manifold characterized by DL-based HAD methods can only reveal a few underlying variation factors of the background distribution and cannot capture the complex structures behind land covers of all categories and 2) DL-based HAD methods trained on anomaly-contaminated HSIs tend to overfit specific anomalies, resulting in poor background characterization. To tackle these issues, this study presents a novel and robust framework for HAD called model-driven deep mixture network (MDMN) that combines the strengths of model-driven and data-driven approaches while emphasizing interpretability. By assuming that the background, consisting of various land-covers, arises from a mixture of low-dimensional manifolds, the MDMN incorporates a novel deep mixture module to comprehensively characterize the background. This module uses a low-dimensional manifold learned by an AE to represent a specific category of background land covers. To mitigate the impact of anomaly corruptions, the MDMN incorporates a convex relaxation of a sparse constraint, which helps prevent overfitting anomalies. Extensive experimental results demonstrate that the proposed MDMN offers more satisfactory and robust detection performance.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Geoscience and Remote Sensing
dc.relation.isbasedon	10.1109/TGRS.2023.3309960
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0404 Geophysics, 0906 Electrical and Electronic Engineering, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.subject.classification	37 Earth sciences
dc.subject.classification	40 Engineering
dc.title	A Model-Driven Deep Mixture Network for Robust Hyperspectral Anomaly Detection
dc.type	Journal Article
utslib.citation.volume	61
utslib.for	0404 Geophysics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0909 Geomatic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-29T08:58:52Z
pubs.publication-status	Published
pubs.volume	61

Abstract:

Hyperspectral anomaly detection (HAD) aims to identify samples with unknown atypical spectra from the background. Deep learning (DL)-based methods, particularly autoencoders (AEs), have proven effective in uncovering the underlying profiles for HAD. However, in real-world applications of hyperspectral images (HSIs), complex background land covers and anomaly corruptions are common, leading to two issues: 1) a low-dimensional manifold characterized by DL-based HAD methods can only reveal a few underlying variation factors of the background distribution and cannot capture the complex structures behind land covers of all categories and 2) DL-based HAD methods trained on anomaly-contaminated HSIs tend to overfit specific anomalies, resulting in poor background characterization. To tackle these issues, this study presents a novel and robust framework for HAD called model-driven deep mixture network (MDMN) that combines the strengths of model-driven and data-driven approaches while emphasizing interpretability. By assuming that the background, consisting of various land-covers, arises from a mixture of low-dimensional manifolds, the MDMN incorporates a novel deep mixture module to comprehensively characterize the background. This module uses a low-dimensional manifold learned by an AE to represent a specific category of background land covers. To mitigate the impact of anomaly corruptions, the MDMN incorporates a convex relaxation of a sparse constraint, which helps prevent overfitting anomalies. Extensive experimental results demonstrate that the proposed MDMN offers more satisfactory and robust detection performance.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178475