Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images
This work addresses annotation efficiency for remote sensing image classification, but it is incremental as it combines existing self-supervised pre-training and active learning methods.
The paper tackles the problem of limited model performance when fine-tuning self-supervised models on small, biased training sets for multi-label classification of remote sensing images by integrating active learning to select informative samples. The result shows that this approach is particularly effective in cases with strong class imbalance, outperforming random selection.
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.