@inproceedings{6d23cad495ed4439bfa557c1f0555ad7,
title = "SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization",
abstract = "In this paper we present a supervised hyperspectral image segmentation algorithm based on a convex formulation of a marginal maximum a posteriori segmentation with hidden fields and structure tensor regularization: Segmentation via the Constraint Split Augmented Lagrangian Shrinkage by Structure Tensor Regularization (SegSALSA-STR). This formulation avoids the generally discrete nature of segmentation problems and the inherent NP-hardness of the integer optimization associated. We extend the Segmentation via the Constraint Split Augmented Lagrangian Shrinkage (SegSALSA) algorithm [1] by generalizing the vectorial total variation prior using a structure tensor prior constructed from a patch-based Jacobian [2]. The resulting algorithm is convex, time-efficient and highly parallelizable. This shows the potential of combining hidden fields with convex optimization through the inclusion of different regularizers. The SegSALSA-STR algorithm is validated in the segmentation of real hyperspectral images.",
keywords = "Constrained Split Augmented Lagrangian Shrinkage Algorithm (SALSA), Image segmentation, hidden fields, structure tensor regularization",
author = "Filipe Condessa and Jose Bioucas-DIas and Jelena Kovacevic",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015 ; Conference date: 02-06-2015 Through 05-06-2015",
year = "2015",
month = jul,
day = "2",
doi = "10.1109/WHISPERS.2015.8075464",
language = "English (US)",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2015 7th Workshop on Hyperspectral Image and Signal Processing",
}