PaTSI: Pattern mining of time series of satellite images in KNIME

TitrePaTSI: Pattern mining of time series of satellite images in KNIME
Publication TypeConference Proceedings
Year of Publication2016
AuthorsCollin, M, Flouvat, F, Selmaoui-Folcher, N
Conference NameIEEE International Conference on Data Mining (ICDM'16), Workshop Demo
Date Published12/2016
Conference LocationBarcelonna, Spanish

In this paper, we present PaTSI, a tool for analyzing evolutions of objects in time series of satellite images
(TSSI). This tool is a plugin integrated in the KNIME Analytics Platform. PaTSI is a workflow composed of several nodes assembled together to form a whole KDD process (data selection, pre-processing, image segmentation, pattern mining and visualization). Input data consists of a TSSI and GIS information on the studied area. This data is transformed in a single attributed directed acyclic graph (a-DAG), where nodes represent objects described by several attributes and edges represent temporal relationships. This graph is then mined to extract frequent evolutions (weighted path patterns) using an efficient graph mining algorithm. At the end of the process, extracted patterns can be filtered using regular expressions and displayed on the original images in order to facilitate experts’ interpretation. In the present demo, the pertinence of PaTSI is illustrated through its application to soil erosion monitoring.