Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTian, Gegeen_US
dc.identifier.citationTian, G. (2022). Land cover classification based on SAR and optical images using ensemble machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractLand cover classification is an important remote sensing application. Satellite optical and radar images are two of the most widely used remote sensing images with respective advantages and disadvantages. Using both radar and optical images and machine learning, this project is to explore automatic land cover classification traditionally done manually or semi-manually. The objective is to study, develop and compare different ensemble/non-ensemble machine learning algorithms for the PolSAR-based crop identification task, and learn the pre-processing procedure and general flow of any land-cover classification based on the remote sensing SAR image data. By analyzing different classification results with various machine learning algorithms, we can get a deep understanding of discriminative machine learning problems.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleLand cover classification based on SAR and optical images using ensemble machine learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLu Yilongen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
TianGege_FYP Report.pdf
  Restricted Access
35.25 MBAdobe PDFView/Open

Page view(s)

Updated on Dec 10, 2023


Updated on Dec 10, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.