CloudMatch: Weak-to-Strong Consistency Learning for Semi-Supervised Cloud Detection

2026-01-07

Journal of Applied Remote Sensing

AI Summary

This work addresses the high cost of pixel-level annotation and heavy reliance on labeled data in cloud detection by proposing CloudMatch, a novel framework that integrates cross-scene and within-scene mixed augmentation to generate semantically consistent yet structurally diverse augmented views. By enforcing consistency learning between a weakly augmented view and two strongly augmented views, and incorporating semi-supervised strategies such as pseudo-labeling, CloudMatch effectively captures the structural diversity and contextual variability of clouds. Evaluated on multiple remote sensing datasets, CloudMatch significantly outperforms existing methods, demonstrating superior accuracy and generalization in semi-supervised cloud detection through efficient utilization of unlabeled data.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Semi-Supervised Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Due to the high cost of annotating accurate pixel-level labels, semi-supervised learning has emerged as a promising approach for cloud detection. In this paper, we propose CloudMatch, a semi-supervised framework that effectively leverages unlabeled remote sensing imagery through view-consistency learning combined with scene-mixing augmentations. An observation behind CloudMatch is that cloud patterns exhibit structural diversity and contextual variability across different scenes and within the same scene category. Our key insight is that enforcing prediction consistency across diversely augmented views, incorporating both inter-scene and intra-scene mixing, enables the model to capture the structural diversity and contextual richness of cloud patterns. Specifically, CloudMatch generates one weakly augmented view along with two complementary strongly augmented views for each unlabeled image: one integrates inter-scene patches to simulate contextual variety, while the other employs intra-scene mixing to preserve semantic coherence. This approach guides pseudolabel generation and enhances generalization. Extensive experiments show that CloudMatch achieves good performance, demonstrating its capability to utilize unlabeled data efficiently and advance semi-supervised cloud detection.

Problem

• semi-supervised learning
• cloud detection
• remote sensing imagery
• pixel-level annotation
• unlabeled data

Innovation

• weak-to-strong consistency
• scene-mixing augmentation
• semi-supervised cloud detection
• inter-scene mixing
• intra-scene mixing