Automatic Hyperspectral Image Clustering Using Qutrit Differential Evolution

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256315" target="_blank" >RIV/61989100:27240/24:10256315 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-981-97-7184-4_24" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-97-7184-4_24</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-97-7184-4_24" target="_blank" >10.1007/978-981-97-7184-4_24</a>

Result language

angličtina

Original language name

Automatic Hyperspectral Image Clustering Using Qutrit Differential Evolution

Original language description

A hyperspectral image serves as a valuable data source for ground cover analysis. However, determining the optimum number of clusters in hyperspectral images faces challenges due to the &quot;curse of dimensionality&quot; and the unavailability of ground truth images. Therefore, employing unsupervised cluster detection methods proves more advantageous in practical scenarios. This paper introduces a qutrit differential evolution algorithm for automatic clustering of hyperspectral images. The proposed algorithm incorporates qutrit Hadamard gates for population initialization and qutrit NOT gates for mutation. A qutrit-based crossover operation is also implemented following the normalization principle. The results of the proposed qutrit differential evolution are compared with the classical and qubit differential evolution algorithms utilizing different statistical tests and the F score. The Adjusted Rand Index serves as the fitness function and is used to validate the clusters. In most cases, the proposed algorithm outperforms the competing algorithms and the K-means algorithm with predefined cluster numbers.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

ADVANCES IN SWARM INTELLIGENCE, PT II, ICSI 2024

ISBN

978-981-9771-83-7

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

15

Pages from-to

280-294

Publisher name

SPRINGER-VERLAG SINGAPORE PTE LTD

Place of publication

SINGAPORE

Event location

Xining

Event date

Aug 23, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001308319900024