Low-quality multivariate spatio-temporal serial data preprocessing

As the accumulation of spatio-temporal data, the low-quality problems of multivariate spatio-temporal data become clear and mainly present that numerous missing data, high noise of time series and great different spatial scale of spatiotemporal data. Aimed at the low-quality problems of multivariate spatio-temporal series, we propose three methods to process them, firstly, using improved non-local means (NLM) algorithm and nonlinear regression analysis method to achieve incomplete data imputation; secondly, using NLM algorithm to deal with Gaussian white noise in time series; thirdly, using the Gaussian pyramid method to zoom the spatial scale for multivariate spatial data. Besides, we compare with traditional methods using quantitative evaluation indices to measure the performance, including K-nearest and wavelet threshold. The experimental simulations indicate that the interpolating accuracy of the two proposed algorithms are higher than K-nearest neighbor algorithm and the effect of denoising using NLM algorithm is obviously better than wavelet threshold method. The experiment results further indicate that the Gaussian pyramid method effectively achieves spatial scale transformation of multivariate spatial data and keeps the local detail characteristics of spatial data with better visual expression effect.