Coarse to fine patches-based multitemporal analysis of very high resolution satellite images

In this paper, a patch based method for multi-temporal analysis of high resolution image is proposed. Conventionally, multi-temporal analysis performed at pixel level suffer from several restrictions, e.g., registration, bi-temporal analysis. To overcome these restrictions, two methods for multi-temporal analysis are proposed at patch level. One is for change detection in time series data by classifying all pairs of patches along time axis in the whole sequence into two classes. Features used for classification are similarity measures based on local statistical models and histogram of local patterns. The other aims at evolution analysis in long image time series. To characterize the evolution patterns, spatio-temporal local pattern features are extracted from time series data. ν-support vector machine (ν-SVM) is applied to classify different kinds of evolution at patch level. Performance is evaluated based on our database produced by iterative classification.