Unsupervised Change Detection in Landsat Images with Atmospheric Artifacts: A Fuzzy Multiobjective Approach

A new unsupervised approach based on a hybrid wavelet transform and Fuzzy Clustering Method (FCM) with Multiobjective PMakale Swarm Optimization (MO-PSO) is proposed to obtain a binary change mask in Landsat images acquired with different atmospheric conditions. The proposed method uses the following steps: preprocessing, classification of preprocessed image, and binary masks fusion. Firstly, a photometric invariant technique is used to transform the Landsat images from RGB to HSV colour space. A hybrid wavelet transform based on Stationary (SWT) and Discrete Wavelet (DWT) Transforms is applied to the hue channel of two Landsat satellite images to create subbands. After that, mean shift clustering method is applied to the subband difference images, computed using the absolute-valued difference technique, to smooth the difference images. Then, the proposed method optimizes iteratively two different fuzzy based objective functions using MO-PSO to evaluate changed and unchanged regions of the smoothed difference images separately. Finally, a fusion approach based on connected component with union technique is proposed to fuse two binary masks to estimate the final solution. Experimental results show the robustness of the proposed method to existence of haze and thin clouds as well as Gaussian noise in Landsat images.

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Yayıncı
(dc.publisher)
Mathematical Problems in Engineering
Haklar
(dc.rights)
Open access
Eser Adı
(dc.title)
Unsupervised Change Detection in Landsat Images with Atmospheric Artifacts: A Fuzzy Multiobjective Approach
Özet
(dc.description.abstract)
A new unsupervised approach based on a hybrid wavelet transform and Fuzzy Clustering Method (FCM) with Multiobjective PMakale Swarm Optimization (MO-PSO) is proposed to obtain a binary change mask in Landsat images acquired with different atmospheric conditions. The proposed method uses the following steps: preprocessing, classification of preprocessed image, and binary masks fusion. Firstly, a photometric invariant technique is used to transform the Landsat images from RGB to HSV colour space. A hybrid wavelet transform based on Stationary (SWT) and Discrete Wavelet (DWT) Transforms is applied to the hue channel of two Landsat satellite images to create subbands. After that, mean shift clustering method is applied to the subband difference images, computed using the absolute-valued difference technique, to smooth the difference images. Then, the proposed method optimizes iteratively two different fuzzy based objective functions using MO-PSO to evaluate changed and unchanged regions of the smoothed difference images separately. Finally, a fusion approach based on connected component with union technique is proposed to fuse two binary masks to estimate the final solution. Experimental results show the robustness of the proposed method to existence of haze and thin clouds as well as Gaussian noise in Landsat images.
Yayın Tarihi
(dc.date.issued)
2018
Kayıt Giriş Tarihi
(dc.date.accessioned)
2019-07-09T12:26:20Z
Açık Erişim tarihi
(dc.date.available)
2019-07-09T12:26:20Z
Yayın Dili
(dc.language.iso)
eng
Konu Başlıkları
(dc.subject)
Change Detection
Atıf için Künye
(dc.identifier.citation)
2
Yayın Türü
(dc.type)
Makale
Yazar/lar
(dc.contributor.author)
YAVARIABDI, Amir
Yazar/lar
(dc.contributor.author)
KUSETOĞULLARI, Hüseyin
Tek Biçim Adres
(dc.identifier.uri)
https://hdl.handle.net/20.500.12498/853
Atıf Dizini
(dc.source.database)
Wos
Atıf Dizini
(dc.source.database)
Scopus
Açıklama
(dc.description)
Highlight: 1) Proposing a new frequency-based unsupervised change detection method for Landsat images which are captured with various atmospheric conditions. 2) Combining a photometric invariant technique with wavelet transforms to decrease the influence of atmospheric conditions on the change detection results. 3) Presenting two fitness cost functions based on FCM which are robust to noise, haze, and thin cloud(s) and being used in the MO-PSO. 4) Presenting a new procedure that focuses on decreasing the computational cost of the population-based optimization algorithms and improving their convergence rate.
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