A new artificial intelligence optimization method for PCA based unsupervised change detection of remote sensing image data

Abstract

In this study, a new artificial intelligence optimization algorithm, Di erential Search (DS), was proposed for Principal Component Analysis (PCA) based unsupervised change detection method for optic and SAR image data. The model firstly computes an eigenvector space using previously created k × k blocks. The change detection map is generated by clustering the feature vector as two clusters which are changed and unchanged using Di erential Search Algorithm. For clustering, a cost function is used based on minimization of Euclidean distance between cluster centers and pixels. Experimental results of optic and SAR images proved that proposed approach is e ective for unsupervised change detection of remote sensing image data.