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

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 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.