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Matteoli ieee gold_2010_clean
- 1. Pisa, 30.11.2007 Stefania Matteoli a Nicola Acito b Marco Diani a Giovanni Corsini a a Dipartimento di Ingegneria dell’Informazione, Università di Pisa, Pisa, Italy b Accademia Navale, Livorno, Italy Livorno, 30.04.2010 Hyperspectral Target Detection via Local Background Suppression South of Italy Chapter Remote Sensing & Image Processing Group
- 2. Background - Hyperspectral Target Detection
- 3. Outline
- 7. Global background vs local background Global background subspace Local background subspace generally Target detection Background suppression
- 12. LBSE procedure
- 13. Target detection step each considered background basis estimation algorithm (N-S, LBSS, and LBSE) should be embodied in a subspace-based target detector Generalized Matched Filter (GMF) defined in the orthogonal complement of the background subspace N-S LBSS LBSE Background suppression Target detection
- 14. Results: 1) LBSE adaptability to spatially variable backgrounds True-color image LBSE map LBSE map histogram urban area: high complexity vegetated rural area: low complexity
- 16. Results: 2) Simulation results (1000 images) Data-set
- 17. Results: 2) Simulation (1000 images), [email_address] D =1 LBSS: K L BSS is a user-specified parameter. No criteria exist to set it and several configurations have to be tested in order to assure good performance.
- 20. Pisa, 30.11.2007 Livorno, 30.04.2010 Thanks for your attention! South of Italy Chapter
Editor's Notes
- As regards background suppression, background subspace can be defined globally over the whole scene, or locally, by considering a neighborhood of each test pixel.
- That is the image where the targets have been inserted. The simulation was conducted for several different values both of FI and ALFA:
- These are detection results. FAR@PD=1, so the lower the better. Specifically we have plots of FAR vs FI and FAR vs ALFA. Results are in favor of the local approach. The global method (blue curve) manages to perform comparably to the local ones for a value of FI=10dB, but its performance quickly degrades for lower FI. (Of course the performance gets better as ALFA increases). Conversely, the local methods manage to detect the target even if it has very low global residual energy. Specifically, LBSE (red curve) provides the best results in most cases. LBSS exhibits a diversity in performance wrt to the number of neighboring pixels considered, for the selection of which no criteria can be invoked. IN fact, here, several configurations had to be tested so as to get good performance.