  This paper analyzes and compares different deep learning loss functions in
the framework of multi-label remote sensing (RS) image scene classification
problems. We consider seven loss functions: 1) cross-entropy loss; 2) focal
loss; 3) weighted cross-entropy loss; 4) Hamming loss; 5) Huber loss; 6)
ranking loss; and 7) sparseMax loss. All the considered loss functions are
analyzed for the first time in RS. After a theoretical analysis, an
experimental analysis is carried out to compare the considered loss functions
in terms of their: 1) overall accuracy; 2) class imbalance awareness (for which
the number of samples associated to each class significantly varies); 3)
convexibility and differentiability; and 4) learning efficiency (i.e.,
convergence speed). On the basis of our analysis, some guidelines are derived
for a proper selection of a loss function in multi-label RS scene
classification problems.
