In this paper,we sampled and transf

In this paper,we sampled and transform ed the remote sensing big data set into a wavelet domain. The statistical characteristics of wavelet coefficients in terms of the scale, time, and band of the data set were comprehensively analyzed and compared. The data set of different textures was decomposed into different scales, and the parameters of the GMM of the wavelet coefficients were estimated. The statistical characteristics of different textures were also compared. We found that the cluster characteristics of the wavelet coefficients are still obvious in the remote sensing big data set for different bands and different scales. However, it is not always well estimated when we modeled the long-term sequence big data set using a GMM. We also found that the features of different textures for the big data set are obviously reflected in the probability density function and model parameters of wavelet coefficients.