Earth Observation Semantics and Data Analytics for Coastal Environmental Areas

Current satellite images provide us with detailed information about the state of our planet, as well as about our technical infrastructure and human activities. A range of already existing commercial and scientific applications try to analyze the physical content and meaning of satellite images by exploiting the data of individual, multiple or temporal sequences of images. However, what we still need today are advanced tools to automatically analyze satellite images in order to extract and understand their full content and meaning. To remedy this exploration problem, we outline a highly automated and application-adapted data-mining and content interpretation system consisting of five main components, namely Data Sources (selection and storage of relevant images), Data Model Generation (patch cutting and generation of feature vectors), Database Management System (systematic data storage), Knowledge Discovery in Databases (clustering and content labeling), and Statistical Analytics (generation of classification maps). As test sites, we selected UNESCO-protected areas in Europe that include coastal areas for monitoring and an area known in the Mediterranean Sea that contains fish cages. The analyzed areas are: the Curonian Lagoon in Lithuania and Russia, the Danube Delta in Romania, the Hardangervidda in Norway, and the Wadden Sea in the Netherlands. For these areas, we are providing the results of our image content classification system consisting of image classification maps and additional statistical analytics based on three different use cases. The first use case is the detection of wind turbines vs. boats in the Wadden Sea. The second use case is the identification of fish cages/aquaculture along the Mediterranean coast. Finally, the third use case describes the differences between beaches, dams, dunes, and tidal flats in the Danube Delta, the Wadden Sea, etc. The average classification accuracy that we obtained is ranging from 80% to 95% depending on the type of available images

Polarimetric SAR Data Feature Selection Using Measures of Mutual Information

Several algorithms for polarimetric synthetic aperture radar (PolSAR) data indexing and classification were proposed in the state of the art literature. In particular, one of them computes powerful, compact feature descriptors composed of the first three logarithmic cumulants of the BiQuaternion Fractional Fourier Transform (BiQFrFT) coefficients of PolSAR patches. Since the BiQFrFT of each patch is computed at three different angles, the algorithm's result consists in nine complex-valued features (18 real-valued features) for single polarization images and in nine biquaternion-valued features (72 real-valued features) for fully polarimetric images. In this paper feature selection based on mutual information (MI) is employed to optimally select a subset of features, in order to improve the indexing performances and to minimize the classification error. The improved results are shown on two polarimetric images: a L-band PALSAR image over Danube's Delta, Romania and a C-band RadarSAT2 image over Brâila, Romania