Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection

Abstract

Bitemporal supervised learning paradigm always dominates remote sensing change detection using numerous labeled bitemporal image pairs, especially for high spatial resolution (HSR) remote sensing imagery. However, it is very expensive and labor-intensive to label change regions in large-scale bitemporal HSR remote sensing image pairs. In this paper, we propose single-temporal supervised learning (STAR) for universal remote sensing change detection from a new perspective of exploiting changes between unpaired images as supervisory signals. STAR enables us to train a high-accuracy change detector only using unpaired labeled images and can generalize to real-world bitemporal image pairs. To demonstrate the flexibility and scalability of STAR, we design a simple yet unified change detector, termed ChangeStar2, capable of addressing binary change detection, object change detection, and semantic change detection in one architecture. ChangeStar2 achieves state-of-the-art performances on eight public remote sensing change detection datasets, covering above two supervised settings, multiple change types, multiple scenarios. The code is available at https://github.com/Z-Zheng/pytorch-change-models.

IJCV 2024. arXiv admin note: text overlap with arXiv:2108.07002

References in corpus (7)

• FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
• Remote Sensing Image Change Detection with Transformers
• SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
• Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
• S2Looking: A Satellite Side-Looking Dataset for Building Change Detection
• Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
• An Empirical Study of Remote Sensing Pretraining