Solar and Remote Sensing Image Analysis

Solar Image Analysis

Solar image analysis research requires the development of reliable coronal hole segmentation methods that are validated by manual segmentations from at-least two different independent experts. UNM developed the manual segmentation software that was used for validation of automated segmentation methods. UNM senior design teams have and continue working on a WebApp to support manual segmentations from a large number of users. Current research is also focused on developing matching algorithms for driving physical models from accurate coronal hole detection from solar observation images.

Journal papers

  1. Jatla, V., Pattichis, M.S., Arge, C.N., “ Detecting Coronal Holes for Solar Activity Modeling,” Under review, IEEE Transactions on Image Processing,

Conference papers

  1. Pattichis, M.S., Hock, R., Jatla, V., Henney, C., Arge, C., “ Detecting Coronal Holes for Solar Activity Modeling,” 2014 Asilomar Conference on Signals, Systems, and Computers, 5 pages, 2014.

Refereed conference presentations

  1. DeMarco, S., Arge, C.N., Pattichis, M.S., Hock, R., and Henney, C.J., “Methods for estimating total open heliospheric magnetic flux,” 2014 American Geological Union Fall Meeting, San Francisco, Dec., 2014.
  2. Pattichis, M.S., Hock, R., Henney, C., Arge, C., DeMarch, S., Delgado, A.P., Darsey, C., and Jatla, V., “Identifying Coronal Holes: Can we learn any lessons from Computer Aided Diagnosis?,” invited presentation, 2014 Solar Heliospheric & Interplanetary Environment (SHINE), Telluride, Colorado, June 2014.

Remote Sensing Research

Prior research in remote sensing imaging was focused on the development of stochastic models for image reconstruction and multispectral image registration using mutual information.

Journal papers

  1. Jeromin, O.M. and Pattichis, M.S., “Multiscale Sampling Geometries and Methods for Deterministic and Stochastic Reconstructions of Magnitude and Phase Spectra of Satellite Imagery,” IEEE Transactions on Geosciences and Remote Sensing, vol. 50, no. 10, pp. 3678-3692, Oct. 2012.
  2. Kern, J. and Pattichis, M.S., “Robust multispectral image registration using mutual information models,” IEEE Transactions on Geosciences and Remote Sensing, vol. 45, no. 5, pp. 1494-1505, May 2007.


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