Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
New change detection method using double segmentation and its application on remotely sensed images
Download
index.pdf
Date
2013
Author
Gedik, Ekin
Metadata
Show full item record
Item Usage Stats
4
views
0
downloads
Change detection research, a branch of statistical data analysis, focuses on detecting changed samples between di erent observations of the same dataset. The proposed study presents a novel change detection procedure and its application as a complete framework which is designed to work on remotely sensed images. The scope of the study is defined as detecting man-made change objects between satellite images of the same region, acquired at di erent times. Proposed framework has three main steps as preprocessing, feature extractionclassification and postprocessing. Preprocessing step normalizes, registers and measures the similarity of image pairs. The main contribution of the proposed study lies at the feature extraction and classification step. With the help of newly proposed "double segmentation" paradigm, an object based approach can be utilized without any prior information or supervision. Well known features in the change detection literature are defined, combined and compared in the study. Apart from known classification methods such as K-Means Clustering and Expectation-Maximization, a novel heuristic thresholding method is also presented. A postprocessing procedure which helps to obtain more accurate and visually appealing results is also provided. Experiments conducted on artificial and real satellite images show that proposed framework is good at capturing the man-made change object characteristics in remotely sensed images with high accuracy.
Subject Keywords
Remote-sensing images
,
Image analysis.
,
Heuristic algorithms.
,
Sampling (Statistics)
URI
http://etd.lib.metu.edu.tr/upload/12616239/index.pdf
https://hdl.handle.net/11511/23055
Collections
Graduate School of Natural and Applied Sciences, Thesis