Designing Forecast Thematic Maps Using Time Series Remotely Sensed Images

In the paper a number of original algorithms implemented as the program system are considered. The algorithms allow to perform the automated forecasting of land use/cover change using time series remotely sensed (RS) images. In the framework of the proposed approach the forecasting performs in two stages. In the first stage time series RS images are classified by the original classification algorithm, which is based upon both statistical nonparametric and artificial neural network (ANN) classification and separate processing of spectral and spatial features of RS images. The complex classification allows significantly decreasing the “noisiness” of the final thematic maps and to increase the classification accuracy in comparison with classification methods of traditional processing, RS and mapping software. To make the interpretation of RS images more flexible and effective it is proposed to perform classification using ANN with original way of forming feature space. In the second stage, the designed raster thematic maps are processed by the enhanced algorithm of time series analysis, which is based upon Markov chains for transition matrix calculation and cellular automata application allowing to take into account not only probability of transition from one class to another but also spatial interclass correlation. The results of effectiveness investigation of the proposed algorithms, obtained with use of model RS images and real time series RS images for Uymon steppe area (Altay Region, Russia) obtained from satellite RESURS-О1 are discussed.