Abstract :
The progress in information retrieval, computer vision, and image analysis makes it possible to establish very complete bases of algorithms and operators. A specialist in remote sensing or image processing now has the tools that allow him, at least in theory, to configure applications solving complex problems of image understanding. However, in reality, earth observation (EO) data analysis is still performed in a very laborious way at the end of repeated cycles of trial and error. To overcome this, we proposed a novel advanced remote sensing information processing system knowledge-driven information mining (KIM). KIM is based on human-centered concepts (HCCs), which implements new features and functions allowing improved feature extraction, search on a semantic level, the availability of collected knowledge, interactive knowledge discovery, and new visual user interfaces. We assess the HCC methodology for solving several difficult tasks in EO image interpretation, using a broad variety of sensor data, from meter-resolution synthetic aperture radar and optical images to hyperspectral data.
Keywords :
computer vision; data acquisition; data analysis; data mining; feature extraction; geophysical signal processing; information retrieval; remote sensing; user interfaces; Earth observation data analysis; Earth observation images; KIM; computer vision; domain ontology; feature extraction; human centered concept; hyperspectral data; image analysis; image interpretation; image processing; image understanding; information retrieval; interactive knowledge discovery; knowledge-driven information mining; optical image; remote sensing information processing system; synthetic aperture radar data; visual user interface; Application software; Computer vision; Earth; Hyperspectral sensors; Image analysis; Image processing; Image retrieval; Information retrieval; Optical sensors; Remote sensing;
