Adaptively Augmented Consistency Learning: A Semi-supervised Segmentation Framework for Remote Sensing

TL;DR

This paper introduces AACL, a semi-supervised framework that improves remote sensing image segmentation accuracy by leveraging unlabeled data with novel augmentation techniques, addressing label scarcity issues.

Contribution

The paper proposes a novel semi-supervised segmentation framework, AACL, utilizing Uniform Strength Augmentation and Adaptive Cut-Mix to enhance remote sensing segmentation performance.

Findings

Achieves up to 20% improvement in specific categories.

Shows a 2% increase in overall performance.

Demonstrates effectiveness across various RS datasets.

Abstract

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.