TY - GEN
T1 - Supervised hyperspectral image segmentation
T2 - 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014
AU - Condessa, Filipe
AU - Bioucas-Dias, José
AU - Kovačević, Jelena
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/6/28
Y1 - 2014/6/28
N2 - Image segmentation is fundamentally a discrete problem. It consists of finding a partition of the image domain such that the pixels in each element of the partition exhibit some kind of similarity. The optimization is obtained via integer optimization which is NP-hard, apart from few exceptions. We sidestep from the discrete nature of image segmentation by formulating the problem in the Bayesian framework and introducing a hidden set of real-valued random fields determining the probability of a given partition. Armed with this model, the original discrete optimization is converted into a convex program. To infer the hidden fields, we introduce the Segmentation via the Constrained Split Augmented Lagrangian Shrinkage Algorithm (SegSALSA). The effectiveness of the proposed methodology is illustrated with hyperspectral image segmentation.
AB - Image segmentation is fundamentally a discrete problem. It consists of finding a partition of the image domain such that the pixels in each element of the partition exhibit some kind of similarity. The optimization is obtained via integer optimization which is NP-hard, apart from few exceptions. We sidestep from the discrete nature of image segmentation by formulating the problem in the Bayesian framework and introducing a hidden set of real-valued random fields determining the probability of a given partition. Armed with this model, the original discrete optimization is converted into a convex program. To infer the hidden fields, we introduce the Segmentation via the Constrained Split Augmented Lagrangian Shrinkage Algorithm (SegSALSA). The effectiveness of the proposed methodology is illustrated with hyperspectral image segmentation.
KW - Constrained Split Augmented Lagrangian Shrinkage Algorithm (SALSA)
KW - Image segmentation
KW - alternating optimization
KW - hidden Markov measure fields
KW - hidden fields
UR - http://www.scopus.com/inward/record.url?scp=85007469567&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007469567&partnerID=8YFLogxK
U2 - 10.1109/WHISPERS.2014.8077490
DO - 10.1109/WHISPERS.2014.8077490
M3 - Conference contribution
AN - SCOPUS:85007469567
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - 2014 6th Workshop on Hyperspectral Image and Signal Processing
PB - IEEE Computer Society
Y2 - 24 June 2014 through 27 June 2014
ER -