Hybrid hesitant fuzzy linguistic bi-objective binary coyote clustering based segmentation and classification for land use land cover in hyperspectral image

Land use and land cover segmentation and classification from hyperspectral image is significant in many land use inventories. Though several techniques have been proposed for classification of land use and land cover, the classification accuracy is low in the prevailing approaches. This manuscript o...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	International journal of information technology (Singapore. Online) Ročník 16; číslo 1; s. 525 - 534
Hlavní autoři:	Yele, Vijaykumar P., Alegavi, Sujata, Sedamkar, R. R.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Nature Singapore 01.01.2024 Springer Nature B.V
Témata:	Accuracy Agriculture Algorithms Artificial Intelligence Biodiversity Classification Climate change Clustering Computer Imaging Computer Science Computing time Datasets Decision making Deep learning Food security Graph neural networks Hyperspectral imaging Image classification Image Processing and Computer Vision Image segmentation Infrastructure Land cover Land use Linguistics Machine Learning Natural resource management Neural networks Noise levels Objectives Optimization Original Research Pattern Recognition and Graphics Preprocessing Remote sensing Segmentation Semantics Signal to noise ratio Software Engineering Urban planning Vegetation Vision Land use and land cover segmentation Anisotropic diffusion Kuwahara filtering Land use and land cover classification Hyperspectral image Binary coyote optimization algorithm
ISSN:	2511-2104, 2511-2112
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!