.png){kind=icon}
Return
Hyperspectral image classification using multiobjective optimization
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-022-12462-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Hyperspectral images constitute a substantial amount of data in the form of spectral bands. This information is used for land cover analysis, specifically in classifying a hyperspectral pixel, which is a popular domain in remote sensing. This paper proposed an efficient framework to classify spectral-spatial hyperspectral images by employing multiobjective optimization. Spectral-spatial features of hyperspectral images are passed for optimization. As hyperspectral images have a high dimensional feature set, many classifiers cannot perform well. Multiobjective optimization reduces the feature set without affecting the discrimination ability of the classifier. The proposed work is validated on a standard hyperspectral image set, Pavia University and Kennedy Space Centre.
Keywords:
Hyperspectral images
Classification
Multiobjective optimization
MOEAD
Journal
IF:
3
Papers:
2.0W
Citations:
3.2W
Organization
Cited Papers
.png){kind=icon}
ShareSaveLocality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis
OAAI
OAAIShareSaveRandom Forest Ensembles and Extended Multiextinction Profiles for Hyperspectral Image Classification
PREAI
ShareSave
View more