  In recent years, deep neural networks (DNNs) have been found very successful
for multi-label classification (MLC) of remote sensing (RS) images.
Self-supervised pre-training combined with fine-tuning on a randomly selected
small training set has become a popular approach to minimize annotation efforts
of data-demanding DNNs. However, fine-tuning on a small and biased training set
may limit model performance. To address this issue, we investigate the
effectiveness of the joint use of self-supervised pre-training with active
learning (AL). The considered AL strategy aims at guiding the MLC fine-tuning
of a self-supervised model by selecting informative training samples to
annotate in an iterative manner. Experimental results show the effectiveness of
applying AL-guided fine-tuning (particularly for the case where strong
class-imbalance is present in MLC problems) compared to the application of
fine-tuning using a randomly constructed small training set.
