MATRIX SENTINEL FREE
The open and free data access provided by many EO missions, e.g., the Copernicus program, allows for the collection of dense multimodal SITS. There is a wide range of applications that benefit from SITS analysis, e.g., agriculture monitoring, monitoring of land resources, estimation of damage caused by natural hazards, and urban development. The analysis of SITS covers not only the extraction of spatial information but reflects the spatio-temporal dynamic evolution of the scene and the objects composing the scene.
MATRIX SENTINEL SERIES
The last few years have witnessed an increased technological development of remote satellite sensors and related derived products, e.g., data cubes, leading to an increased interest in developing new tools and methods for unsupervised satellite image time series (SITS) analysis and, more generally, Earth Observation (EO). The effectiveness of the proposed methodology is shown in two use-case scenarios, namely flooding and landslide events, for which a joint acquisition of Sentinel-1 and Sentinel-2 images is performed. Furthermore, the proposed inter-modality translation allows the usage of standard unsupervised clustering approaches (e.g., K-means using the Dynamic Time Warping measure) for mono-modal SITS analysis. In this regard, we provide an extension of the matrix profile concept, which represents an answer to identifying differences and to discovering novelties in time series. Inter-modality translation is achieved by means of a Generative Adversarial Network (GAN) architecture, whereas, for the identification of anomalies caused by natural hazards, we adapt the task to a similarity search in SITS. The unsupervised analysis of multimodal SITS proposed in this paper follows a two-step methodology: (i) inter-modality translation and (ii) the identification of anomalies in a change-detection framework. The variability in terms of the characteristics of the satellite sensors requires the existence of algorithms that allow the integration of multiple modalities and the identification of anomalous spatio-temporal evolutions caused by natural hazards. The technological development of the remote sensing domain led to the acquisition of satellite image time series (SITS) for Earth Observation (EO) by a variety of sensors.