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

Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images