Please use this identifier to cite or link to this item: https://zone.biblio.laurentian.ca/handle/10219/2616
Title: Aerial image segmentation
Authors: Althwaini, Abdulkareem Ali
Item Type: Thesis
Degree: Master of Science (MSc) in Computational Sciences
Keywords: Aerial image segmentation;smoothing filters;k-means classifying;c-means fuzzy classifying
Issue Date: 25-Jul-2016
Abstract: Image segmentation plays a vital role in applications such as remote sensing. For this example, remote sensing, aerial image segmentation is a special case of image segmentation. There are some unique features of aerial images, like noise in natural landscapes, which need to be addressed in order to obtain an optimal solution. Bushes and rocks are examples of landscape features with diverse and variable pixel values that need to be distinguished by the segmentation process. Smoothing filters present a common solution to address the problem of noise in images, as does aerial image segmentation. There are several image segmentation techniques used for aerial image segmentation. Some of these techniques are more sensitive to noise problems, and are necessary to discriminate between different smoothing filters. In this thesis, a number of different aspects of aerial image segmentation and their solutions are explained. In addition to this, a novel smoothing filter is introduced and compared with other methods using different segmentation techniques. Finally, all of the previous points are applied to a real world problem.
URI: https://zone.biblio.laurentian.ca/handle/10219/2616
Appears in Collections:Master's theses
Master's Theses

Files in This Item:
File Description SizeFormat 
Abdulkareem Althwaini Thesis Final.pdf15.17 MBAdobe PDFView/Open


Items in LU|ZONE|UL are protected by copyright, with all rights reserved, unless otherwise indicated.