Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158216
Title: Land cover classification based on SAR and optical images using ensemble machine learning
Authors: Tian, Gege
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Tian, G. (2022). Land cover classification based on SAR and optical images using ensemble machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158216
Project: A3138-211
Abstract: Land 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.
URI: https://hdl.handle.net/10356/158216
Fulltext Permission: restricted
Fulltext Availability: With 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)

23
Updated on Dec 1, 2022

Download(s)

5
Updated on Dec 1, 2022

Google ScholarTM

Check

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