Satellite image time series clustering under collaborative principal component analysis

Abstract

Compared with one single image, satellite image time series (SITS) can capture the dynamic changes in land cover types, thus achieving a more comprehensive and accurate land cover classification map. Due to decades of data acquisition and new high temporal resolution sensors, SITS is becoming more available. Corresponding SITS analysis techniques need to be further developed. Most satellite images are multispectral, namely, multivariate. However, multivariate time series analysis techniques are less mature compared with univariate time series. There seems to be a lack of a robust and accurate similarity measure between multivariate time series for SITS clustering. In this paper, we propose a novel method to transform multivariate SITS into univariate SITS while the useful information is kept as much as possible. And then advanced univariate time series similarity measures can be adopted to achieve SITS clustering. The proposed method is tested on Landsat-TM SITS dataset and shows a better clustering result than ordinary multivariate time series similarity measure. In addition, the overall computing time may be reduced due to dimension reduction.