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Full-Text Articles in Geographic Information Sciences
Guidance In Feature Extraction To Resolve Uncertainty, Boris Kovalerchuk, Michael Kovalerchuk, Simon Streltsov, Matthew Best
Guidance In Feature Extraction To Resolve Uncertainty, Boris Kovalerchuk, Michael Kovalerchuk, Simon Streltsov, Matthew Best
Computer Science Faculty Scholarship
Automated Feature Extraction (AFE) plays a critical role in image understanding. Often the imagery analysts extract features better than AFE algorithms do, because analysts use additional information. The extraction and processing of this information can be more complex than the original AFE task, and that leads to the “complexity trap”. This can happen when the shadow from the buildings guides the extraction of buildings and roads. This work proposes an AFE algorithm to extract roads and trails by using the GMTI/GPS tracking information and older inaccurate maps of roads and trails as AFE guides.
Automated Vector-To-Raster Image Registration, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Robert Brigantic, Michael Kovalerchuk, Brian Graff
Automated Vector-To-Raster Image Registration, Boris Kovalerchuk, Peter Doucette, Gamal Seedahmed, Robert Brigantic, Michael Kovalerchuk, Brian Graff
Computer Science Faculty Scholarship
The variability of panchromatic and multispectral images, vector data (maps) and DEM models is growing. Accordingly, the requests and challenges are growing to correlate, match, co-register, and fuse them. Data to be integrated may have inaccurate and contradictory geo-references or not have them at all. Alignment of vector (feature) and raster (image) geospatial data is a difficult and time-consuming process when transformational relationships between the two are nonlinear. The robust solutions and commercial software products that address current challenges do not yet exist. In the proposed approach for Vector-to-Raster Registration (VRR) the candidate features are auto-extracted from imagery, vectorized, and …