Unsupervised Spatiotemporal Mining of Satellite Image Time Series Using Grouped Frequent Sequential Patterns

Abstract

International audienceAn important aspect of satellite image time series is the simultaneous access to spatial and temporal information. Various tools allow end users to interpret these data without having to browse the whole data set. In this paper, we intend to extract, in an unsupervised way, temporal evolutions at the pixel level and select those covering at least a minimum surface and having a high connectivity measure. To manage the huge amount of data and the large number of potential temporal evolutions, a new approach based on data-mining techniques is presented. We have developed a frequent sequential pattern extraction method adapted to that spatiotemporal context. A successful application to crop monitoring involving optical data is described. Another application to crustal deformation monitoring using synthetic aperture radar images gives an indication about the generic nature of the proposed approach

Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets

“…For example, Julea et al (2011) proposed a grouped frequent sequence pattern mining algorithm for agricultural monitoring, which was aimed at extracting an evolution of each grid pixel with time series images [25]; Romani et al (2013) developed a RemoteAgri system to discover the Plateau-Valley-Mountain (P-V-M) association patterns for monitoring sugar cane fields with time series of remote sensing images and found that the P-V-M pattern mainly analyzed the association patterns between two geographical parameters [26]; and Saulquin et al (2014) designed an eventbased mining algorithm for dealing with SST anomalies relative to ENSO events, which considers each one-dimensional time series as a series of significant time-scale events for each grid pixel [12]. Generally, each grid pixel may have several patterns, and each pattern may evolve several geographical parameters; therefore, the complicated association patterns from remote sensing images make it impossible for a user to analyze an entire set and find the most interesting ones [27].…”

Cyclic Repeated Patterns in Sequential Pattern Mining Based on the Fuzzy C-Means Clustering and Association Rule Mining Technique

“…A typical base of sequences is a set of sequences of discrete events, in that each system has a sole sequence identifier [6]. In particular, closed sequential pattern mining has become adynamic topic in data mining community, as it is a compressed yet lossless compression of sequential patterns [5].…”

A Remote-Sensing-Driven System for Mining Marine Spatiotemporal Association Patterns

“…For example, Datcu et al [25] demonstrated a prototype of a knowledge-driven content-based information mining system to manage large volumes of remote sensing images, and Zhang et al [26] designed a visual data mining system with two classes of components for classifying remotely sensed images and exploring image classification processes. Julea et al [27] proposed a frequent sequence pattern mining algorithm for agricultural monitoring, which aimed to extract the evolution of each grid pixel from a time series of images. Korting et al [28] proposed and implemented a new toolbox, Geographic Data Mining Analyst (GeoDMA), which integrates a series of processes including segmentation, feature extraction, feature selection, landscape and multi-temporal features, as well as data mining for pattern recognition and multi-temporal analysis of remote sensing imagery.…”

“…Examples include image database management [25], image classification [26,28], applications and domains (e.g., evolution in a given location [27]), and one-to-one relationships [11,29]. Given the complexity of marine environments, the above systems and tools must overcome great challenges to achieve the following: (i) to extract marine objects, events, and processes from remote sensing images, and then to represent and store them; (ii) to design mining strategies to explore association patterns among events, processes, and among multiple parameters; and (iii) to visualize such association patterns.…”