Classification of Landsat 8 Images Using Convolutional Neural Network Based on Minimum Noise Fraction Transform · Alnoor Digital Repository

Title: Classification of Landsat 8 Images Using Convolutional Neural Network Based on Minimum Noise Fraction Transform
Abstract: —The use of remote sensing methods has transformed
environmental management and regional planning by allowing the
identification of items or phenomena on the Earth's surface.
However, noise in picture data remains a chronic difficulty in this
discipline, compromising spatial resolution and object detection
accuracy.
The purpose of this study is to improve the classification
accuracy of Landsat 8 pictures by developing a Convolutional
Neural Network (CNN) based on the Minimum Noise Fraction
(MNF) transform. The goal is to evaluate MNF's efficacy in
compressing and organizing multispectral images, hence reducing
the influence of noise on picture categorization.
The MNF transform is used to Landsat 8 image data to remove
noisy bands before adopting CNN as a supervised classification
approach. The current study takes use of CNN's inherent benefits
in dealing with high-dimensional data, learning complicated
representations, and automatically extracting key features from
pictures, while simultaneously evaluating MNF's efficiency in
increasing image quality.
The findings show that using MNF as a preprocessing step
produces images with improved quality and organization.
Subsequent classification using CNN obtained an astounding
accuracy of 97.41%, with a great representation of the study
region and varied land use categories, highlighting the synergy
between MNF and CNN in improving classification performance.
The article suggests that combining MNF transform with CNN
enhances classification accuracy of Landsat 8 pictures, with
positive implications for developments in environmental
monitoring, land use mapping, and remote sensing technologies.
Scientific Type: غير معروف
Link: https://fruct.org/publications/volume-35/fruct35/
Journal volume: vol.35,No.1
Collaboration type: مشترك
Publish Date: April 26, 2024
Participated Universities (Publication): Alnoor University
Scopus status: In Scopus
Scopus index year: 2 024
Scopus citation score: 0
Clarivate status: Not In Clarivate
Pub. Med. status: Not In PubMed
Author (Publication): الاء سالم عبد الرزاق حسن
Journal (Publication): PROCEEDING OF THE 35TH CONFERENCE OF FRUCT ASSOCIATION
Publisher (Publication): IEEE Computer Society
ISSN: 2305-7254
Country (Publication): Russian Federation
Country type: عالمية
College (Publication): College of Dentistry
Departement (Publication): Department of Dentistry

Media: Academic paper