Gated Graph Attention-based Crossover Snake (GGA-CS) Algorithm for Hyperspectral Image Classification

R. Ablin () and G. Prabin
Additional contact information
R. Ablin: Arunachala College of Engineering for Women
G. Prabin: Arunachala College of Engineering for Women

Annals of Data Science, 2025, vol. 12, issue 1, No 12, 305 pages

Abstract: Abstract Hyperspectral image classification involves assigning pixels or regions within a hyperspectral image to specific classes or categories based on the spectral information captured across multiple bands. Traditional method faces several challenges such as High Dimensionality, Scalability, Spectral Variability, as well as Limited Contextual Information. Hence to solve these issues a novel Gated Graph Attention-based Crossover Snake (GGA-CS) algorithm is proposed for classifying hyperspectral images. In this work, a Graph Neural Network (GNN) is employed to capture both spectral and spatial relationships between pixels, and a gated attention mechanism is utilized to enhance specific spectral bands. After the training process, a crossover-based snake optimization is applied that tuned the parameter and obtain classification output of GNN and adjust the pixels to enhance the performances of GGA-CS method. The study is validated on diverse datasets namely the Indian Pines dataset, the University of Pavia dataset, as well as Salinas dataset. The evaluation of the GGA-CS method’s performance includes assessing its effectiveness using key metrics. Comparisons with state-of-the-art methods are conducted to gauge its efficacy in hyperspectral image classification, as demonstrated by experimental results.

Keywords: Hyperspectral image classification; Attention mechanism; Graph Neural Network; Crossover-based snake optimization; Spectral variability (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-024-00567-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:12:y:2025:i:1:d:10.1007_s40745-024-00567-8

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-024-00567-8

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().