An Adaptive Spatial Scan Density Estimation Method
Ramani S. Pilla, (Case Western Reserve University), email@example.com,
Peng Tao, (Accu Image Diagnostics Corporation), firstname.lastname@example.org, and
Carey Priebe, (Johns Hopkins University), email@example.com
Spatial scan density (SSD) estimation via mixture models is an important problem in the field of spatial statistical analysis and has wide applications in image analysis. The ``borrowed strength'' density estimation (BSDE) method via mixture models (Priebe, 1996) enables one to estimate the local probability density function in a random field wherein potential similarities between the density functions for the subregions are exploited. This article proposes an efficient method for SSD estimation by integrating the borrowed strength technique into the alternative EM framework (Pilla & Lindsay, 2001) which combines the statistical basis of the BSDE approach with the stability and improved convergence rate of the alternative EM methodology. In addition, we propose an adaptive SSD estimation method that extends the aforementioned approach by eliminating the need to find the posterior probability of membership of the component densities afresh in each subregion. Simulation results and an application to the detection and identification of man-made regions of interest in an unmanned aerial vehicle imagery experiment show that the adaptive method significantly outperforms the BSDE method. Other applications include automatic target recognition, mammographic image analysis and minefield detection.