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We present a supervised hyperspectral image segmentation algorithm based on aconvex formulation of a marginal maximum a posteriori segmentation with hiddenfields and structure tensor regularization: Segmentation via the ConstraintSplit Augmented Lagrangian Shrinkage by Structure Tensor Regularization(SegSALSA-STR). This formulation avoids the generally discrete nature ofsegmentation problems and the inherent NP-hardness of the integer optimizationassociated.  We extend the Segmentation via the Constraint Split Augmented LagrangianShrinkage (SegSALSA) algorithm by generalizing the vectorial total variationprior using a structure tensor prior constructed from a patch-based Jacobian.The resulting algorithm is convex, time-efficient and highly parallelizable.This shows the potential of combining hidden fields with convex optimizationthrough the inclusion of different regularizers. The SegSALSA-STR algorithm isvalidated in the segmentation of real hyperspectral images.

SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization