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

G. Swetha;Rajeshreddy Datla;Sobhan Babu;C. Krishna Mohan
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Abstract

Scene classification in remote sensing (RS) images is a challenging task due to the limited availability of labeled data and the high intraclass 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 (PL), a widely used SSL technique, determines suitable labels to unlabeled data based on high-confidence model predictions. However, traditional PL methods suffer from confirmation bias, where incorrect labels reinforce errors, degrading model performance. To address this, we propose contrapositive learning-based PL (CPL-PL), a novel method designed specifically for RS scene classification. CPL-PL introduces a contrapositive loss (CPLoss) 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 RS datasets demonstrate that CPL-PL significantly outperforms conventional PL strategies, especially in low-label regimes. The proposed method provides a promising direction for advancing semi-supervised scene classification in RS images.
基于对置学习的遥感图像半监督场景分类伪标记
由于标记数据的可用性有限和复杂景观的高类内变异性,遥感图像的场景分类是一项具有挑战性的任务。半监督学习(SSL)已经成为利用有限的标记数据来利用大量未标记数据来改进分类的有效方法。伪标记(PL)是一种广泛使用的SSL技术,它根据高置信度模型预测为未标记的数据确定合适的标签。然而,传统的PL方法存在确认偏差,其中不正确的标签会强化错误,降低模型性能。为了解决这个问题,我们提出了一种专门用于RS场景分类的基于对置学习的pls (cpll -PL)方法。cpll - pl引入了对负损失(CPLoss),它在确保不同场景的表示分离的同时,强制相似场景的特征一致性,从而导致更可靠的伪标签分配。我们的方法减轻了伪标签噪声,增强了特征识别,提高了分类鲁棒性。在基准RS数据集上的实验结果表明,cpll -PL显著优于传统的PL策略,特别是在低标签状态下。该方法为推进遥感图像的半监督场景分类提供了一个有希望的方向。
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