G. Swetha;Rajeshreddy Datla;Sobhan Babu;C. Krishna Mohan
{"title":"基于对置学习的遥感图像半监督场景分类伪标记","authors":"G. Swetha;Rajeshreddy Datla;Sobhan Babu;C. Krishna Mohan","doi":"10.1109/LGRS.2025.3583475","DOIUrl":null,"url":null,"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.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CPL-PL: Contrapositive Learning-Based Pseudo-Labeling for Semi-Supervised Scene Classification in Remote Sensing Images\",\"authors\":\"G. Swetha;Rajeshreddy Datla;Sobhan Babu;C. Krishna Mohan\",\"doi\":\"10.1109/LGRS.2025.3583475\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":91017,\"journal\":{\"name\":\"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society\",\"volume\":\"22 \",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11052310/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11052310/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
CPL-PL: Contrapositive Learning-Based Pseudo-Labeling for Semi-Supervised Scene Classification in Remote Sensing Images
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.