Spectral data provide opportunities to discriminate targets from background clutter and to detect partially concealed objects. However, data with high spatial resolution generally are not synoptic scale (hundreds of kilometers). By analyzing courser resolution synoptic imagery, high-resolution sensors can then be cued to areas of potential targets. Multispectral images (Landsat 7) are combined with ancillary data-lines of communication, digital elevation models (DEMS), etc.-in order to characterize the scene of interest. Two scenes were examined: regions of the Balkans (Kosovo, Bosnia-Herzegovina, Montenegro, and Serbia), and Iraq. Three data products result from this fusion of data sources: (1) land cover classification, (2) trafficability analysis, and (3) "hide area" delineation. In addition, the fusion of imagery with elevation models provides a beneficial perspective to the analyst. The classification is a thematic map of the different land cover types produced through a combination of supervised and unsupervised means. Trafficability may be dependent on a number of factors including land cover type, vegetation density, soil moisture content, and access to major roads or navigable rivers. In addition to land cover-based trafficability analysis, terrain-based trafficability uses DEM-derived slope information to determine vehicle accessibility. Determination of "hide areas" may be another important product. These can be defined by their proximity to the forest perimeter, access to roads, and the underlying terrain. As a result of these analyses carried out on the synoptic scale, the collective size of the areas of interest provided to the next sensor in the intelligence chain may be greatly reduced.


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    Title :

    Exploitation of Landsat imagery and ancillary data for battlespace characterization


    Contributors:
    Orloff, S.M. (author) / Su May Hsu, (author) / Burke, H.-H.K. (author)


    Publication date :

    2002-01-01


    Size :

    374080 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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