CPL-PL: Contrapositive Learning-Based Pseudo-Labeling for Semi-Supervised Scene Classification in Remote Sensing Images

Scene classification in remote sensing images is a challenging task due to the limited availability of labeled data and the high intra-class variability in complex landscapes. Semi-supervised learning (SSL) has emerged as an effective approach to leverage the limited labeled data in utilizing a large amount of unlabeled data for improved classification. Pseudo-labeling, a widely used SSL technique, determines suitable labels to unlabeled data based on high-confidence model predictions. However, traditional pseudo-labeling methods suffer from confirmation bias, where incorrect labels reinforce errors, degrading model performance. To address this, we propose Contrapositive Learning-based Pseudo-Labeling (CPL-PL), a novel method designed specifically for remote sensing scene classification. CPL-PL introduces a Contrapositive Loss that enforces feature consistency for similar scenes while ensuring representation separation for dissimilar ones, leading to more reliable pseudo-label assignments. Our approach mitigates pseudo-label noise, enhances feature discrimination, and improves classification robustness. Experimental results on benchmark remote sensing datasets demonstrate that CPL-PL significantly outperforms conventional pseudo-labeling strategies, especially in low-label regimes. The proposed method provides a promising direction for advancing semi-supervised scene classification in remote sensing images.