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A 3D solid model-aided object cueing method that matches phase angles of directional derivative vectors at image pixels to phase angles of vectors normal to projected model edges is described. It is intended for finding specific types of objects at arbitrary position and orientation in overhead images, independent of spatial resolution, obliqueness, acquisition conditions, and type of imaging sensor. It is shown that the phase similarity measure can be efficiently evaluated over all combinations of model position and orientation using the FFT. The highest degree of similarity over all model orientations is captured in a match surface of similarity values vs. model position. Unambiguous peaks in this surface are sorted in descending order of similarity value, and the small image thumbnails that contain them are presented to human analysts for inspection in sorted order.

The Image Content Engine (ICE) is being developed to provide cueing assistance to human image analysts faced with increasingly large and intractable amounts of image data. The ICE architecture includes user configurable feature extraction pipelines which produce intermediate feature vector and match surface files which can then be accessed by interactive relational queries. Application of the feature extraction algorithms to large collections of images may be extremely time consuming and is launched as a batch job on a Linux cluster. The query interface accesses only the intermediate files and returns candidate hits nearly instantaneously. Queries may be posed for individual objects or collections. The query interface prompts the user for feedback, and applies relevance feedback algorithms to revise the feature vector weighting and focus on relevant search results. Examples of feature extraction and both model-based and search-by-example queries are presented.

A new approach to fusion of information from multispectral (MS) imagery is presented. This approach is motivated by the desire to develop an unsupervised classifier which can robustly detect settled regions (i.e., regions containing man-made structures). It is well known in the remote sensing community that the ground truth used in supervised classification are not only difficult to obtain but tend to be inconsistent and difficult to validate. In this paper, we present an unsupervised approach for the problem which combines a two-step analysis of MS imagery with a spatial analysis of higher resolution panchromatic imagery. The MS analysis first combines the multispectral pixel information to create an image of pixel labels generated by the K-Means clustering algorithm. Tile-based features are then computed based on the first and second order class label statistics. These tile features are then used to classify the tiles via a second application of the K-Means clustering algorithm. The results from this MS analysis define clusters of tiles of variable texture that are highly likely to contain evidence of human settlements. Spatial information is then brought to bear by analyzing coregistered high-resolution panchromatic images. By standard detectors we find the corner and edge densities in each coregistered tile. Determination of the threshold values used to determine the presence or absence of human settlements is currently performed by a human observer. The results of this spatial analysis are then compared and combined with the MS results to finally determine the set of tiles containing signs of human settlements.

The automated production of maps of human settlement from recent satellite images is essential to detailed studies of urbanization, population movement, and the like. Commercial satellite imagery is becoming available with sufficient spectral and spatial resolution to apply computer vision techniques previously considered only for laboratory (high resolution, low noise) images. In this paper we attempt to extract human settlement from IKONOS 4-band and panchromatic images using spectral segmentation together with a form of generalized second-order statistics and detection of edges, corners, and other candidate human-made features in the imagery.