Enhancing hyperspectral remote sensing image classification using robust learning technique

Hameed, Alaa Ali

doi:10.1016/j.jksus.2023.102981

Journal of King Saud University - Science

2024

DOI: 10.1016/j.jksus.2023.102981

|View full text |Cite

Enhancing hyperspectral remote sensing image classification using robust learning technique

Alaa Ali Hameed

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A New Hyperspectral Image Identification Method Based on LSDA and OSELM

Zhou,

Yang,

Duan

et al. 2024

Modelling and Simulation in Engineering

View full text Add to dashboard Cite

To solve the problems of information loss and nonlinear transformation of hyperspectral image dimensionality reduction methods and the problem that recognition methods are sensitive to noise and cannot be trained online, a new hyperspectral image identification method based on locality sensitive discriminant analysis (LSDA) and online sequential extreme learning machine (OSELM) is proposed. Firstly, LSDA is used to reduce the redundant information and data noise, and the local structure and discriminant information of the hyperspectral image are extracted on the basis of reducing the data dimension. Then, a real‐time recognition model based on OSELM is constructed to identify various terrain targets in hyperspectral images through online real‐time updates of data. According to the experimental results of the Pavia University dataset and Salinas dataset, the proposed LSDA‐OSELM not only achieved the maximum overall accuracy (OA) of 97.56 and 97.88 but also obtained the maximum kappa coefficient of 0.9726 and 0.9774. Compared with several new dimensionality reduction methods and identification methods, the proposed LSDA‐OSELM has achieved a leading position in both OA and kappa indicators. The proposed method improves the generalization ability and accuracy of hyperspectral image target recognition models. In the future, its computational complexity and parameter robustness are expected to be further improved and applied in practical application scenarios.

show abstract

A New Hyperspectral Image Identification Method Based on LSDA and OSELM

Zhou,

Yang,

Duan

et al. 2024

Modelling and Simulation in Engineering

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Enhancing hyperspectral remote sensing image classification using robust learning technique

Cited by 1 publication

References 19 publications

A New Hyperspectral Image Identification Method Based on LSDA and OSELM

A New Hyperspectral Image Identification Method Based on LSDA and OSELM

Contact Info

Product

Resources

About