Cluster Kernel For Learning Similarities Between Symmetric Positive Definite Matrix Time Series

Abstract

The launch of the last generation of Earth observation satellites has yield to a great improvement in the capabilities of acquiring Earth surface images, providing series of multitemporal images. To process these time series images, many machine learning algorithms have been proposed in the literature such as warping based methods and recurrent neural networks (LSTM,…). Recently, based on an ensemble learning approach, the time series cluster kernel (TCK) has been proposed and has shown competitive results compared to the state-of-the-art. Unfortunately, it does not model the spectral/spatial dependencies. To overcome this problem, this paper aims at extending the TCK approach by modeling the time series of second-order statistical features (SO-TCK). Experimental results are conducted on different benchmark datasets, and land cover classification with remote sensing satellite time series over the Reunion Island.