# A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification

Source: [A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification](<https://www.sota2.com/research/paper/68fa6e671e209131cea3fbef>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:56:59.119Z

Authors: Hichame Yessou, Gencer Sumbul, Beg\\"um Demir

Published: 2020-09-29T11:35:26.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2009.13935>)

[PDF](<https://arxiv.org/pdf/2009.13935.pdf>)