用自适应增强提升遥感图像分割，少标注也能高精度。
Adaptively Augmented Consistency Learning: A Semi-supervised Segmentation Framework for Remote Sensing
- 引入统一强度增强与自适应裁剪混合，挖掘未标注图像信息。
- 在多个遥感数据集上，特定类别性能提升最高达20%。
- 适合标注稀缺的遥感图像分割任务，如灾害监测与资源管理。
遥感（RS）通过远距离获取地物或区域数据，用于环境监测、资源管理和灾害响应。其分割任务面临高质量标注图像稀缺的问题，因遥感图像多样复杂，像素级标注困难，制约了监督分割算法的发展。为此，本文提出自适应增强一致性学习（AACL），一种半监督分割框架，在标注数据有限条件下提升遥感图像分割精度。AACL利用统一强度增强（USAug）和自适应裁剪混合（AdaCM）从无标签图像中提取额外信息。在多个遥感数据集上的评估表明，AACL在半监督分割中表现优异，特定类别性能最高提升20%，整体性能较当前最优框架提高2%。
原文摘要 · Abstract (English)
Remote sensing (RS) involves the acquisition of data about objects or areas from a distance, primarily to monitor environmental changes, manage resources, and support planning and disaster response. A significant challenge in RS segmentation is the scarcity of high-quality labeled images due to the diversity and complexity of RS image, which makes pixel-level annotation difficult and hinders the development of effective supervised segmentation algorithms. To solve this problem, we propose Adaptively Augmented Consistency Learning (AACL), a semi-supervised segmentation framework designed to enhances RS segmentation accuracy under condictions of limited labeled data. AACL extracts additional information embedded in unlabeled images through the use of Uniform Strength Augmentation (USAug) and Adaptive Cut-Mix (AdaCM). Evaluations across various RS datasets demonstrate that AACL achieves competitive performance in semi-supervised segmentation, showing up to a 20% improvement in specific categories and 2% increase in overall performance compared to state-of-the-art frameworks.
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