UNSUPERVISED CHANGE DETECTION IN OPTICAL SATELLITE IMAGERY USING SIFT FLOW

The process of identifying change in remote sensing images has been a focal point of research for decades now. Many classical algorithms exist, and many new modern ones are still being developed. These algorithms can be divided into supervised and unsupervised. In this work an unsupervised method is...

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Main Authors: B. Awad, I. Erer
Format: Article
Language:English
Published: Copernicus Publications 2022-07-01
Series:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-M-2-2022/47/2022/isprs-archives-XLVI-M-2-2022-47-2022.pdf
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author B. Awad
I. Erer
author_facet B. Awad
I. Erer
author_sort B. Awad
collection DOAJ
description The process of identifying change in remote sensing images has been a focal point of research for decades now. Many classical algorithms exist, and many new modern ones are still being developed. These algorithms can be divided into supervised and unsupervised. In this work an unsupervised method is presented. This method relies on the scene alignment algorithm SIFT flow. It is shown that building upon simple principles an accurate change map can be obtained from the SIFT descriptor flow of the two input images. Furthermore, it is shown that this method despite its simplicity exceeds other unsupervised methods and comes close to supervised ones, even exceeding them in some metrics. Lastly, the advantages of SIFT flow in comparison to the supervised methods are highlighted alongside its own downsides.
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spelling doaj.art-5f41344d70244c87999b10194d0f2ff92022-12-22T01:55:51ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342022-07-01XLVI-M-2-2022475210.5194/isprs-archives-XLVI-M-2-2022-47-2022UNSUPERVISED CHANGE DETECTION IN OPTICAL SATELLITE IMAGERY USING SIFT FLOWB. Awad0I. Erer1ITU, Electrical and Electronic Engineering Faculty, Electronics and Communication Department, 34469 Maslak, Istanbul, TürkiyeITU, Electrical and Electronic Engineering Faculty, Electronics and Communication Department, 34469 Maslak, Istanbul, TürkiyeThe process of identifying change in remote sensing images has been a focal point of research for decades now. Many classical algorithms exist, and many new modern ones are still being developed. These algorithms can be divided into supervised and unsupervised. In this work an unsupervised method is presented. This method relies on the scene alignment algorithm SIFT flow. It is shown that building upon simple principles an accurate change map can be obtained from the SIFT descriptor flow of the two input images. Furthermore, it is shown that this method despite its simplicity exceeds other unsupervised methods and comes close to supervised ones, even exceeding them in some metrics. Lastly, the advantages of SIFT flow in comparison to the supervised methods are highlighted alongside its own downsides.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-M-2-2022/47/2022/isprs-archives-XLVI-M-2-2022-47-2022.pdf
spellingShingle B. Awad
I. Erer
UNSUPERVISED CHANGE DETECTION IN OPTICAL SATELLITE IMAGERY USING SIFT FLOW
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
title UNSUPERVISED CHANGE DETECTION IN OPTICAL SATELLITE IMAGERY USING SIFT FLOW
title_full UNSUPERVISED CHANGE DETECTION IN OPTICAL SATELLITE IMAGERY USING SIFT FLOW
title_fullStr UNSUPERVISED CHANGE DETECTION IN OPTICAL SATELLITE IMAGERY USING SIFT FLOW
title_full_unstemmed UNSUPERVISED CHANGE DETECTION IN OPTICAL SATELLITE IMAGERY USING SIFT FLOW
title_short UNSUPERVISED CHANGE DETECTION IN OPTICAL SATELLITE IMAGERY USING SIFT FLOW
title_sort unsupervised change detection in optical satellite imagery using sift flow
url https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-M-2-2022/47/2022/isprs-archives-XLVI-M-2-2022-47-2022.pdf
work_keys_str_mv AT bawad unsupervisedchangedetectioninopticalsatelliteimageryusingsiftflow
AT ierer unsupervisedchangedetectioninopticalsatelliteimageryusingsiftflow