FRFT-based improved algorithm of unsupervised change detection in SAR images via PCA and K-means clustering

This paper presents an improved algorithm of unsupervised change detection technique by taking the same low-order fractional Fourier transform (FRFT) on multitemporal images acquired on the same geographical area but at different time instances, then generates the difference image by the absolute log-ratio operator. In order to acquire the eigenvector space, we perform principal component analysis (PCA) on m × m nonoverlapping difference image blocks. The feature vectors are extracted using m × m data blocks projection onto eigenvector space. The change detection map is generated by clustering the feature vectors using k-means algorithm into two disjoint classes: changed and unchanged. The final results obtained by the improved algorithm exhibited lower error than its preexistence.