Focused information learning method for change detection based on segmentation with limited annotations

Abstract

Recent advancements have significantly improved the field of segmentation-based change detection, particularly in the context of remote-sensing images. However, change detection datasets generally lack segmentation annotations, and the required labeling process is resource-intensive. We propose an improved change detection method based on segmentation to address this challenge. First, change detection annotations are converted to incomplete segmentation annotations through label matching. During segmentation, we utilize the focused information-guided segmentation method (FIGS) and a greenness index to provide prior information during training, guiding the model using accurately labeled regions. Finally, we generate a change map using pretrained features obtained from the segmentation stage. We demonstrate the robustness of our proposed label-matching process by comparing the results to a correctly matched dataset and show that incorporating FIGS and the greenness index improves the segmentation performance. Our method achieves effective change detection results even in scenarios associated with a shortage of annotations.

Keywords

• Building detection
• Change detection
• Data labeling
• Deep learning
• Remote sensing
• Segmentation