Bitemporal multispectral images unsupervised change detection based on undecimated wavelet transform and chi-squared transform

Ordinary unsupervised change detection methods are mostly applied to the magnitude of spectral change vector (MSCV) of bitemporal images. However, the information contained in the different bands of the difference image is not fully exploited. We propose an unsupervised change detection method based on undecimated wavelet transform (UWT) in combination with chi-squared transform (CT). First, the UWT is applied to the difference image, and multiresolution images of each band of the difference image are obtained. Second, spatial constraint CT (SCT) is applied to the same resolution levels of each band of the difference image. Then, the results of SCT at all resolution levels are fused by a majority voting rule. After that, the fusion result as an initial solution of change detection is further improved by a Markov random field (MRF) model. In the implementation of the MRF model, the nonnegative matrix factorization is applied to fuse the MSCV and spectral angle mapper of bitemporal images to obtain the difference image. The experiments on three sets of multitemporal and multispectral images indicate that the proposed method performs well in comprehensive indices compared with other methods. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).