Compressive Pattern Matching on Multispectral Data

Sylvain Rousseau (1) , David Helbert (2, 3) , Philippe Carré (1) , Jacques Blanc-Talon (4)

1 XLIM-SIC - SIC
2 XLIM - XLIM
3 UP - Université de Poitiers = University of Poitiers
4 DGA - Délégation générale de l'armement

We introduce a new constrained minimization problem that performs template and pattern detection on a multispectral image in a compressive sensing context. We use an original minimization problem from Guo and Osher that uses L1 minimization techniques to perform template detec- tion in a multispectral image. We first adapt this minimization problem to work with compressive sensing data. Then we extend it to perform pat- tern detection using a formal transform called the spectralization along a pattern. That extension brings out the problem of measurement recon- struction. We introduce shifted measurements that allow us to reconstruct all the measurement with a small overhead and we give an optimality constraint for simple patterns. We present numerical results showing the performances of the original minimization problem and the compressed ones with different measurement rates and applied on remotely sensed data.

Mots clés: multispectral image; compressed sensing; pattern detection

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Sylvain Rousseau, David Helbert, Philippe Carré, Jacques Blanc-Talon. Compressive Pattern Matching on Multispectral Data. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52 (12), pp.7581 - 7592. ⟨10.1109/TGRS.2014.2314483⟩. ⟨hal-00959680⟩