Using attribute trees to analyse auroral appearance over Canada

Modern space research uses both satellite-born and ground-based instruments to measure the near-Earth space environment. Studying the auroral display provides information of the electric currents in the ionosphere, which is why automated imaging stations capture millions of auroral all-sky images every year. However, due to the nature of the aurora, these images are difficult to analyse automatically: photon-limited images are noisy, and objects are irregular and difficult to identify. We used hierarchical attribute trees in a large scale experiment with over 350,000 auroral all-sky images. Tree-to-tree distances were utilised in classifying images and in locating similar images in content-based image retrieval fashion.

[1]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[2]  Markus Peura Attribute trees in image analysis - heuristic matching and learning techniques , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[3]  Linda G. Shapiro,et al.  A Flexible Image Database System for Content-Based Retrieval , 1999, Comput. Vis. Image Underst..

[4]  Tuija I. Pulkkinen,et al.  Determining the skeletons of the auroras , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[5]  Soon Ae Chun,et al.  An experimental study on content-based image classification for satellite image databases , 2002, IEEE Trans. Geosci. Remote. Sens..

[6]  C. W. Therrien,et al.  Decision, Estimation and Classification: An Introduction to Pattern Recognition and Related Topics , 1989 .

[7]  Mihai Datcu,et al.  Spatial information retrieval from remote-sensing images. II. Gibbs-Markov random fields , 1998, IEEE Trans. Geosci. Remote. Sens..

[8]  A. G. McNamara,et al.  Canopus — A ground-based instrument array for remote sensing the high latitude ionosphere during the ISTP/GGS program , 1995 .

[9]  Paolo Gamba,et al.  Query-by-shape in meteorological image archives using the point diffusion technique , 2001, IEEE Trans. Geosci. Remote. Sens..

[10]  M. Syrjäsuo,et al.  A search engine for auroral forms , 2001 .

[11]  M. Syrjäsuo,et al.  Analysis of Auroral Images: Detection and Tracking , 2002 .

[12]  M. Syrjäsuo,et al.  A statistical study of evening sector arcs and electrojets , 2001 .

[13]  Carla E. Brodley,et al.  Local versus global features for content-based image retrieval , 1998, Proceedings. IEEE Workshop on Content-Based Access of Image and Video Libraries (Cat. No.98EX173).

[14]  Markus Peura,et al.  Image analysis by means of attribute trees-remote sensing applications , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[15]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[16]  Mihai Datcu,et al.  Spatial information retrieval from remote-sensing images. I. Information theoretical perspective , 1998, IEEE Trans. Geosci. Remote. Sens..