Adaptively Augmented Consistency Learning: A Semi-supervised Segmentation Framework for Remote Sensing
This addresses the challenge of limited labeled images for remote sensing segmentation, which is crucial for environmental monitoring and disaster response, but it appears incremental as it builds on existing semi-supervised techniques.
The paper tackles the problem of scarce labeled data in remote sensing segmentation by proposing Adaptively Augmented Consistency Learning (AACL), a semi-supervised framework that achieves up to a 20% improvement in specific categories and a 2% increase in overall performance compared to state-of-the-art methods.
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.