From data to information and knowledge for geospatial applications
Abstract
An ever-increasing number of airborne and spaceborne data-acquisition missions with various sensors produce a glut of data. Sensory data rarely contains information in a explicit form such that an application can directly use it. The processing and analyzing of data constitutes a real bottleneck; therefore, automating the processes of gaining useful information and knowledge from the raw data is of paramount interest. This presentation is concerned with the transition from data to information and knowledge. With data we refer to the sensor output and we notice that data provide very rarely direct answers for applications. For example, a pixel in a digital image or a laser point from a LIDAR system (data) have no direct relationship with elevation changes of topographic surfaces or the velocity of a glacier (information, knowledge). We propose to employ the computer vision paradigm to extract information and knowledge as it pertains to a wide range of geoscience applications. After introducing the paradigm we describe the major steps to be undertaken for extracting information and knowledge from sensory input data. Features play an important role in this process. Thus we focus on extracting features and their perceptual organization to higher order constructs. We demonstrate these concepts with imaging data and laser point clouds. The second part of the presentation addresses the problem of combining data obtained by different sensors. An absolute prerequisite for successful fusion is to establish a common reference frame. We elaborate on the concept of sensor invariant features that allow the registration of such disparate data sets as aerial/satellite imagery, 3D laser point clouds, and multi/hyperspectral imagery. Fusion takes place on the data level (sensor registration) and on the information level. We show how fusion increases the degree of automation for reconstructing topographic surfaces. Moreover, fused information gained from the three sensors results in a more abstract surface representation with a rich set of explicit surface information that can be readily used by an analyst for applications such as change detection.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2006
- Bibcode:
- 2006AGUFMIN41C..03S
- Keywords:
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- 0525 Data management;
- 0530 Data presentation and visualization;
- 0540 Image processing;
- 0594 Instruments and techniques;
- 1621 Cryospheric change (0776)