Unsupervised Change Detection in Landsat Images with Atmospheric Artifacts: A Fuzzy Multiobjective Approach
Kusetogullari, Huseyin and Yavariabdi, Amir
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Abstract
A new unsupervised approach based on a hybrid wavelet transform and
Fuzzy Clustering Method (FCM) with Multiobjective Particle 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: (1) preprocessing, (2)
classification of preprocessed image, and (3) binary masks fusion.
Firstly, a photometric invariant technique is used to transform the
Landsat images from RGB to HSV ccolour 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.... Show more Show less