Forest Disturbance Classification Under Imbalanced and Small-Sample Conditions Based on Collaborative Semi-Supervised Learning and Sample Generation.
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| Title: | Forest Disturbance Classification Under Imbalanced and Small-Sample Conditions Based on Collaborative Semi-Supervised Learning and Sample Generation. |
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| Authors: | Liu, Yudan1 (AUTHOR), Zhao, Yuxin1 (AUTHOR), Yan, Yan1 (AUTHOR), Shao, Yan1 (AUTHOR), Qu, Xinqi1 (AUTHOR), Wu, Ling1 (AUTHOR) wuling@cugb.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1579. 22p. |
| Subjects: | Data augmentation, Probabilistic generative models, Machine learning, Data distribution, Remote sensing, Supervised learning |
| Abstract: | Highlights: What are the main findings? The proposed framework for multi-type forest disturbance classification based on collaborative semi-supervised learning and sample generation enables effective use of unlabeled data and suppresses pseudo-label noise. The method achieves robust classification under imbalanced and small-sample conditions, with an overall accuracy of 93.2%, outperforming single classification methods. The latent diffusion model effectively addresses class imbalance by generating high-fidelity pseudo-samples for rare disturbance types. The generated samples are consistent with real spectral and spatial patterns, improve class balance, and further enhance classification performance. What are the implications of the main findings? The study provides a practical solution for forest disturbance classification when labeled samples are scarce and class distribution is highly imbalanced. Provides a practical and generalizable method for precision forestry; it could be extended to other remote sensing tasks with similar small sample and imbalance issues. The method has the potential to improve forest management, carbon accounting, and ecological assessment. Accurate and timely information on forest disturbance drivers is important for sustainable forest management, global carbon cycle accounting, and climate change response. However, forest disturbance classification is difficult due to two major challenges: limited labeled samples and highly imbalanced disturbance class distribution. In this article, a new framework for multi-type forest disturbance classification based on collaborative semi-supervised learning and sample generation was proposed. First, forest disturbance is detected using long-term remote sensing time series data and disturbance detection algorithms. Spatiotemporal, spectral and terrain features of different disturbance types are extracted. On this basis, to address the problem of imbalanced and small-sample conditions, a collaborative classification strategy is developed. Based on a small number of labeled samples, Support Vector Machine (SVM) and Random Forest (RF) are used to build dual base classifiers. A confident learning (CL) framework is applied to select high-confidence pseudo-labeled samples from unlabeled data. Then, a latent diffusion model (LDM) is introduced to generate high-fidelity pseudo-samples. This increases the sample size and balances the class distribution. Based on the augmented dataset, the dual classifiers are iteratively optimized using a co-training strategy, which improves model generalization under complex conditions. The results show that the proposed framework could generate high-quality pseudo-samples and effectively reduce class imbalance. The overall accuracy (OA) of the proposed framework reaches 93.2%, which is 5.7% and 4.4% higher than single classifier baselines, respectively. After introducing the LDM-based balancing mechanism, performance is further improved by 1.8% compared with the pure semi-supervised framework. This study provides an efficient and reliable solution for large-scale forest ecosystem monitoring. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The proposed framework for multi-type forest disturbance classification based on collaborative semi-supervised learning and sample generation enables effective use of unlabeled data and suppresses pseudo-label noise. The method achieves robust classification under imbalanced and small-sample conditions, with an overall accuracy of 93.2%, outperforming single classification methods. The latent diffusion model effectively addresses class imbalance by generating high-fidelity pseudo-samples for rare disturbance types. The generated samples are consistent with real spectral and spatial patterns, improve class balance, and further enhance classification performance. What are the implications of the main findings? The study provides a practical solution for forest disturbance classification when labeled samples are scarce and class distribution is highly imbalanced. Provides a practical and generalizable method for precision forestry; it could be extended to other remote sensing tasks with similar small sample and imbalance issues. The method has the potential to improve forest management, carbon accounting, and ecological assessment. Accurate and timely information on forest disturbance drivers is important for sustainable forest management, global carbon cycle accounting, and climate change response. However, forest disturbance classification is difficult due to two major challenges: limited labeled samples and highly imbalanced disturbance class distribution. In this article, a new framework for multi-type forest disturbance classification based on collaborative semi-supervised learning and sample generation was proposed. First, forest disturbance is detected using long-term remote sensing time series data and disturbance detection algorithms. Spatiotemporal, spectral and terrain features of different disturbance types are extracted. On this basis, to address the problem of imbalanced and small-sample conditions, a collaborative classification strategy is developed. Based on a small number of labeled samples, Support Vector Machine (SVM) and Random Forest (RF) are used to build dual base classifiers. A confident learning (CL) framework is applied to select high-confidence pseudo-labeled samples from unlabeled data. Then, a latent diffusion model (LDM) is introduced to generate high-fidelity pseudo-samples. This increases the sample size and balances the class distribution. Based on the augmented dataset, the dual classifiers are iteratively optimized using a co-training strategy, which improves model generalization under complex conditions. The results show that the proposed framework could generate high-quality pseudo-samples and effectively reduce class imbalance. The overall accuracy (OA) of the proposed framework reaches 93.2%, which is 5.7% and 4.4% higher than single classifier baselines, respectively. After introducing the LDM-based balancing mechanism, performance is further improved by 1.8% compared with the pure semi-supervised framework. This study provides an efficient and reliable solution for large-scale forest ecosystem monitoring. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101579 |
